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Advances of Machine and Deep Learning in the Health Domain
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Advances of Machine and Deep Learning in the Health Domain

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 76374

Special Issue Editors

Dr. Antonio Celesti

E-Mail Website
Guest Editor
Department of Mathematics, Computer Science, Physics and Hearth Sciences (MIFT), University of Messina, 98166 Messina, Italy
Interests: distributed systems; cloud computing; edge computing; Internet of Things (IoT); machine learning; assistive technology; eHealth
Special Issues, Collections and Topics in MDPI journals
Dr. Ivanoe De Falco

E-Mail Website
Guest Editor
Institute of High Performance Computing and Networking of National Research Council (ICAR-CNR), 80131 Naples, Italy
Interests: artificial intelligence; machine learning; soft computing; computational intelligence; parallel and distributed computing; explainable artificial intelligence; AI/ML applications to eHealth and mobile health; pattern recognition; signal processing; optimization; classification; regression; time series forecasting
Special Issues, Collections and Topics in MDPI journals
Dr. Antonino Galletta

E-Mail Website
Guest Editor
MIFT Department, University of Messina, 98166 Messina, Italy
Interests: security on cloud edge and IoT; ehealth;
Special Issues, Collections and Topics in MDPI journals
Dr. Giovanna Sannino

E-Mail Website
Guest Editor
Institute of High Performance Computing and Networking – National Research Council of Italy (ICAR-CNR), 80131 Naples, Italy
Interests: eHealth; mobile health; signal processing; pattern recognition; biomechanical and physiological parameter extraction and analysis; statistical analysis; machine learning/artificial intelligence techniques for eHealth applications; ICT-based intelligent solutions for chronic disease (cardiovascular diseases); wearable devices (ECG sensors, accelerometer sensors, etc.)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 1st edition of the IEEE International Conference on ICT Solutions for eHealth (ICTS4eHealth) will be held on 5–8 September 2021 in Athens (Greece) in conjunction with the 26th IEEE Symposium on Computers and Communications (ISCC).

For more information about the conference, please use this link:

https://www.icts4ehealth.icar.cnr.it/

Machine and Deep Learning deal with data, and one of their goals is to extract information and related knowledge that is hidden in them in order to make detections and/or predictions and, subsequently, take decisions. With the terms “Machine and Deep Learning”, we cover a wide range of theories, methods, algorithms, and architectures that are used to this end.

This Special Issue will cover promising developments in the related areas of machine and deep learning applied to the health domain and offer possible paths for the future.

The authors of selected papers that are presented at the International IEEE ICTS4eHealth Conference 2021 are invited to submit their extended versions to this Special Issue of the journal Computers after the conference. Submitted papers should be extended to the size of regular research or review articles, with at least 50% extension of new results. All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in open access format in Computers and collected together in this Special Issue’s website. Accepted extended papers will be free of charge. There are no page limitations for this journal.

We are also inviting original research work covering novel theories, innovative methods, and meaningful applications that can potentially lead to significant advances in artificial intelligence in the health domain.

The main topics include but are not limited to:

  • Knowledge management of health data;
  • Data mining and knowledge discovery in healthcare;
  • Machine and deep learning approaches for health data;
  • Explainable ai models for health, biology, and medicine;
  • Decision support systems for healthcare and wellbeing;
  • AI for precision medicine;
  • Optimization for healthcare problems;
  • Regression and forecasting for medical and/or biomedical signals;
  • Healthcare information systems;
  • Wellness information systems;
  • Medical signal and image processing and techniques;
  • Medical expert systems;
  • Diagnoses and therapy support systems;
  • Biomedical applications;
  • Applications of AI in healthcare and wellbeing systems;
  • machine learning-based medical systems;
  • medical data and knowledge bases;
  • neural networks in medicine;
  • ambient intelligence and pervasive computing in medicine and healthcare;
  • AI in genomics;
  • AI for healthcare social networks.

Dr. Antonio Celesti
Dr. Ivanoe De Falco
Dr. Antonino Galletta
Dr. Giovanna Sannino
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

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Editorial

Jump to: Research, Review

4 pages, 169 KiB  
Editorial
Special Issue “Advances in Machine and Deep Learning in the Health Domain”
by Antonio Celesti, Ivanoe De Falco, Antonino Galletta and Giovanna Sannino
Computers 2023, 12(7), 135; https://doi.org/10.3390/computers12070135 - 4 Jul 2023
Viewed by 1153
Abstract
Machine and deep learning techniques are fuelling a revolution in the health domain and are attracting the interest of many cross-disciplinary research groups all over the world [...] Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)

Research

Jump to: Editorial, Review

11 pages, 1262 KiB  
Article
Physical Activity Recommendation System Based on Deep Learning to Prevent Respiratory Diseases
by Usharani Bhimavarapu, M. Sreedevi, Nalini Chintalapudi and Gopi Battineni
Computers 2022, 11(10), 150; https://doi.org/10.3390/computers11100150 - 11 Oct 2022
Cited by 9 | Viewed by 4811
Abstract
The immune system can be compromised when humans inhale excessive cooling. Physical activity helps a person’s immune system, and influenza seasonally affects immunity and respiratory tract illness when there is no physical activity during the day. Whenever people chill excessively, they become more [...] Read more.
The immune system can be compromised when humans inhale excessive cooling. Physical activity helps a person’s immune system, and influenza seasonally affects immunity and respiratory tract illness when there is no physical activity during the day. Whenever people chill excessively, they become more susceptible to pathogens because they require more energy to maintain a healthy body temperature. There is no doubt that exercise improves the immune system and an individual’s fitness. According to an individual’s health history, lifestyle, and preferences, the physical activity framework also includes exercises to improve the immune system. This study developed a framework for predicting physical activity based on information about health status, preferences, calorie intake, race, and gender. Using information about comorbidities, regions, and exercise/eating habits, the proposed recommendation system recommends exercises based on the user’s preferences. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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16 pages, 1840 KiB  
Article
Assessment of Multi-Layer Perceptron Neural Network for Pulmonary Function Test’s Diagnosis Using ATS and ERS Respiratory Standard Parameters
by Ahmad A. Almazloum, Abdel-Razzak Al-Hinnawi, Roberto De Fazio and Paolo Visconti
Computers 2022, 11(9), 130; https://doi.org/10.3390/computers11090130 - 29 Aug 2022
Cited by 4 | Viewed by 4032
Abstract
The aim of the research work is to investigate the operability of the entire 23 pulmonary function parameters, which are stipulated by the American Thoracic Society (ATS) and the European Respiratory Society (ERS), to design a medical decision support system capable of classifying [...] Read more.
The aim of the research work is to investigate the operability of the entire 23 pulmonary function parameters, which are stipulated by the American Thoracic Society (ATS) and the European Respiratory Society (ERS), to design a medical decision support system capable of classifying the pulmonary function tests into normal, obstructive, restrictive, or mixed cases. The 23 respiratory parameters specified by the ATS and the ERS guidelines, obtained from the Pulmonary Function Test (PFT) device, were employed as input features to a Multi-Layer Perceptron (MLP) neural network. Thirteen possible MLP Back Propagation (BP) algorithms were assessed. Three different categories of respiratory diseases were evaluated, namely obstructive, restrictive, and mixed conditions. The framework was applied on 201 PFT examinations: 103 normal and 98 abnormal cases. The PFT decision support system’s outcomes were compared with both the clinical truth (physician decision) and the PFT built-in diagnostic software. It yielded 92–99% and 87–92% accuracies on the training and the test sets, respectively. An 88–94% area under the receiver operating characteristic curve (ROC) was recorded on the test set. The system exceeded the performance of the PFT machine by 9%. All 23 ATS\ERS standard PFT parameters can be used as inputs to design a PFT decision support system, yielding a favorable performance compared with the literature and the PFT machine’s diagnosis program. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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17 pages, 1094 KiB  
Article
Interpretable Lightweight Ensemble Classification of Normal versus Leukemic Cells
by Yúri Faro Dantas de Sant’Anna, José Elwyslan Maurício de Oliveira and Daniel Oliveira Dantas
Computers 2022, 11(8), 125; https://doi.org/10.3390/computers11080125 - 19 Aug 2022
Cited by 1 | Viewed by 2431
Abstract
The lymphocyte classification problem is usually solved by deep learning approaches based on convolutional neural networks with multiple layers. However, these techniques require specific hardware and long training times. This work proposes a lightweight image classification system capable of discriminating between healthy and [...] Read more.
The lymphocyte classification problem is usually solved by deep learning approaches based on convolutional neural networks with multiple layers. However, these techniques require specific hardware and long training times. This work proposes a lightweight image classification system capable of discriminating between healthy and cancerous lymphocytes of leukemia patients using image processing and feature-based machine learning techniques that require less training time and can run on a standard CPU. The features are composed of statistical, morphological, textural, frequency, and contour features extracted from each image and used to train a set of lightweight algorithms that classify the lymphocytes into malignant or healthy. After the training, these classifiers were combined into an ensemble classifier to improve the results. The proposed method has a lower computational cost than most deep learning approaches in learning time and neural network size. Our results contribute to the leukemia classification system, showing that high performance can be achieved by classifiers trained with a rich set of features. This study extends a previous work by combining simple classifiers into a single ensemble solution. With principal component analysis, it is possible to reduce the number of features used while maintaining a high accuracy. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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32 pages, 5708 KiB  
Article
A Novel Criticality Analysis Technique for Detecting Dynamic Disturbances in Human Gait
by Shadi Eltanani, Tjeerd V. olde Scheper and Helen Dawes
Computers 2022, 11(8), 120; https://doi.org/10.3390/computers11080120 - 3 Aug 2022
Cited by 3 | Viewed by 2210
Abstract
The application of machine learning (ML) has made an unprecedented change in the field of medicine, showing a significant potential to automate tasks and to achieve objectives that are closer to human cognitive capabilities. Human gait, in particular, is a series of continuous [...] Read more.
The application of machine learning (ML) has made an unprecedented change in the field of medicine, showing a significant potential to automate tasks and to achieve objectives that are closer to human cognitive capabilities. Human gait, in particular, is a series of continuous metabolic interactions specific for humans. The need for an intelligent recognition of dynamic changes of gait enables physicians in clinical practice to early identify impaired gait and to reach proper decision making. Because of the underlying complexity of the biological system, it can be difficult to create an accurate detection and analysis of imbalanced gait. This paper proposes a novel Criticality Analysis (CA) methodology as a feasible method to extract the dynamic interactions involved in human gait. This allows a useful scale-free representation of multivariate dynamic data in a nonlinear representation space. To quantify the effectiveness of the CA methodology, a Support Vector Machine (SVM) algorithm is implemented in order to identify the nonlinear relationships and high-order interactions between multiple gait data variables. The gait features extracted from the CA method were used for training and testing the SVM algorithm. The simulation results of this paper show that the implemented SVM model with the support of the CA method increases the accuracy and enhances the efficiency of gait analysis to extremely high levels. Therefore, it can perform as a robust classification tool for detection of dynamic disturbances of biological data patterns and creates a tremendous opportunity for clinical diagnosis and rehabilitation. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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19 pages, 5147 KiB  
Article
Automated Detection of Improper Sitting Postures in Computer Users Based on Motion Capture Sensors
by Firgan Feradov, Valentina Markova and Todor Ganchev
Computers 2022, 11(7), 116; https://doi.org/10.3390/computers11070116 - 20 Jul 2022
Cited by 11 | Viewed by 3443
Abstract
Prolonged computer-related work can be linked to musculoskeletal disorders (MSD) in the upper limbs and improper posture. In this regard, we report on developing resources supporting improper posture studies based on motion capture sensors. These resources were used to create a baseline detector [...] Read more.
Prolonged computer-related work can be linked to musculoskeletal disorders (MSD) in the upper limbs and improper posture. In this regard, we report on developing resources supporting improper posture studies based on motion capture sensors. These resources were used to create a baseline detector for the automated detection of improper sitting postures, which was next used to evaluate the applicability of Hjorth’s parameters—Activity, Mobility and Complexity—on the specific classification task. Specifically, based on accelerometer data, we computed Hjorth’s time-domain parameters, which we stacked as feature vectors and fed to a binary classifier (kNN, decision tree, linear SVM and Gaussian SVM). The experimental evaluation in a setup involving two different keyboard types (standard and ergonomic) validated the practical worth of the proposed sitting posture detection method, and we reported an average classification accuracy of up to 98.4%. We deem that this research contributes toward creating an automated system for improper posture monitoring for people working on a computer for prolonged periods. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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17 pages, 2527 KiB  
Article
Automated Detection of Left Bundle Branch Block from ECG Signal Utilizing the Maximal Overlap Discrete Wavelet Transform with ANFIS
by Bassam Al-Naami, Hossam Fraihat, Hamza Abu Owida, Khalid Al-Hamad, Roberto De Fazio and Paolo Visconti
Computers 2022, 11(6), 93; https://doi.org/10.3390/computers11060093 - 10 Jun 2022
Cited by 19 | Viewed by 3322
Abstract
Left bundle branch block (LBBB) is a common disorder in the heart’s electrical conduction system that leads to the ventricles’ uncoordinated contraction. The complete LBBB is usually associated with underlying heart failure and other cardiac diseases. Therefore, early automated detection is vital. This [...] Read more.
Left bundle branch block (LBBB) is a common disorder in the heart’s electrical conduction system that leads to the ventricles’ uncoordinated contraction. The complete LBBB is usually associated with underlying heart failure and other cardiac diseases. Therefore, early automated detection is vital. This work aimed to detect the LBBB through the QRS electrocardiogram (ECG) complex segments taken from the MIT-BIH arrhythmia database. The used data contain 2655 LBBB (abnormal) and 1470 normal signals (i.e., 4125 total signals). The proposed method was employed in the following steps: (i) QRS segmentation and filtration, (ii) application of the Maximal Overlapped Discrete Wavelet Transform (MODWT) on the ECG R wave, (iii) selection of the detailed coefficients of the MODWT (D2, D3, D4), kurtosis, and skewness as extracted features to be fed into the Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier. The obtained results proved that the proposed method performed well based on the achieved sensitivity, specificity, and classification accuracies of 99.81%, 100%, and 99.88%, respectively (F-Score is equal to 0.9990). Our results showed that the proposed method was robust and effective and could be used in real clinical situations. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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15 pages, 4109 KiB  
Article
How Machine Learning Classification Accuracy Changes in a Happiness Dataset with Different Demographic Groups
by Colm Sweeney, Edel Ennis, Maurice Mulvenna, Raymond Bond and Siobhan O’Neill
Computers 2022, 11(5), 83; https://doi.org/10.3390/computers11050083 - 23 May 2022
Cited by 15 | Viewed by 3612
Abstract
This study aims to explore how machine learning classification accuracy changes with different demographic groups. The HappyDB is a dataset that contains over 100,000 happy statements, incorporating demographic information that includes marital status, gender, age, and parenthood status. Using the happiness category field, [...] Read more.
This study aims to explore how machine learning classification accuracy changes with different demographic groups. The HappyDB is a dataset that contains over 100,000 happy statements, incorporating demographic information that includes marital status, gender, age, and parenthood status. Using the happiness category field, we test different types of machine learning classifiers to predict what category of happiness the statements belong to, for example, whether they indicate happiness relating to achievement or affection. The tests were initially conducted with three distinct classifiers and the best performing model was the convolutional neural network (CNN) model, which is a deep learning algorithm, achieving an F1 score of 0.897 when used with the complete dataset. This model was then used as the main classifier to further analyze the results and to establish any variety in performance when tested on different demographic groups. We analyzed the results to see if classification accuracy was improved for different demographic groups, and found that the accuracy of prediction within this dataset declined with age, with the exception of the single parent subgroup. The results also showed improved performance for the married and parent subgroups, and lower performances for the non-parent and un-married subgroups, even when investigating a balanced sample. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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18 pages, 7844 KiB  
Article
A Transfer-Learning-Based Novel Convolution Neural Network for Melanoma Classification
by Mohammad Naved Qureshi, Mohammad Sarosh Umar and Sana Shahab
Computers 2022, 11(5), 64; https://doi.org/10.3390/computers11050064 - 26 Apr 2022
Cited by 11 | Viewed by 3259
Abstract
Skin cancer is one of the most common human malignancies, which is generally diagnosed by screening and dermoscopic analysis followed by histopathological assessment and biopsy. Deep-learning-based methods have been proposed for skin lesion classification in the last few years. The major drawback of [...] Read more.
Skin cancer is one of the most common human malignancies, which is generally diagnosed by screening and dermoscopic analysis followed by histopathological assessment and biopsy. Deep-learning-based methods have been proposed for skin lesion classification in the last few years. The major drawback of all methods is that they require a considerable amount of training data, which poses a challenge for classifying medical images as limited datasets are available. The problem can be tackled through transfer learning, in which a model pre-trained on a huge dataset is utilized and fine-tuned as per the problem domain. This paper proposes a new Convolution neural network architecture to classify skin lesions into two classes: benign and malignant. The Google Xception model is used as a base model on top of which new layers are added and then fine-tuned. The model is optimized using various optimizers to achieve the maximum possible performance gain for the classifier output. The results on ISIC archive data for the model achieved the highest training accuracy of 99.78% using Adam and LazyAdam optimizers, validation and test accuracy of 97.94% and 96.8% using RMSProp, and on the HAM10000 dataset utilizing the RMSProp optimizer, the model achieved the highest training and prediction accuracy of 98.81% and 91.54% respectively, when compared to other models. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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16 pages, 1450 KiB  
Article
Window-Based Multi-Objective Optimization for Dynamic Patient Scheduling with Problem-Specific Operators
by Ali Nader Mahmed and M. N. M. Kahar
Computers 2022, 11(5), 63; https://doi.org/10.3390/computers11050063 - 25 Apr 2022
Cited by 3 | Viewed by 2121
Abstract
The problem of patient admission scheduling (PAS) is a nondeterministic polynomial time (NP)-hard combinatorial optimization problem with numerous constraints. Researchers have divided the constraints of this problem into hard (i.e., feasible solution) and soft constraints (i.e., quality solution). The majority of research has [...] Read more.
The problem of patient admission scheduling (PAS) is a nondeterministic polynomial time (NP)-hard combinatorial optimization problem with numerous constraints. Researchers have divided the constraints of this problem into hard (i.e., feasible solution) and soft constraints (i.e., quality solution). The majority of research has dealt with PAS using integer linear programming (ILP) and single objective meta-heuristic searching-based approaches. ILP-based approaches carry high computational demand and the risk of non-feasibility for a large dataset. In a single objective optimization, there is a risk of local minima due to the non-convexity of the problem. In this article, we present the first pareto front-based optimization for PAS using set of meta-heuristic approaches. We selected four multi-objective optimization methods. Problem-specific operators were developed for each of them. Next, we compared them with single objective optimization approaches, namely, simulated annealing and particle swarm optimization. In addition, this article also deals with the dynamical aspect of this problem by comparing historical window-based decomposition with day decomposition, as has previously been proposed in the literature. An evaluation of the models proposed in the article and comparison with traditional models reveals the superiority of our proposed multi-objective optimization with window incorporation in terms of optimality. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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15 pages, 2479 KiB  
Article
Towards Accurate Skin Lesion Classification across All Skin Categories Using a PCNN Fusion-Based Data Augmentation Approach
by Esther Chabi Adjobo, Amadou Tidjani Sanda Mahama, Pierre Gouton and Joël Tossa
Computers 2022, 11(3), 44; https://doi.org/10.3390/computers11030044 - 16 Mar 2022
Cited by 9 | Viewed by 3141
Abstract
Deep learning models yield remarkable results in skin lesions analysis. However, these models require considerable amounts of data, while accessibility to the images with annotated skin lesions is often limited, and the classes are often imbalanced. Data augmentation is one way to alleviate [...] Read more.
Deep learning models yield remarkable results in skin lesions analysis. However, these models require considerable amounts of data, while accessibility to the images with annotated skin lesions is often limited, and the classes are often imbalanced. Data augmentation is one way to alleviate the lack of labeled data and class imbalance. This paper proposes a new data augmentation method based on image fusion technique to construct large dataset on all existing tones. The fusion method consists of a pulse-coupled neural network fusion strategy in a non-subsampled shearlet transform domain and consists of three steps: decomposition, fusion, and reconstruction. The dermoscopic dataset is obtained by combining ISIC2019 and ISIC2020 Challenge datasets. A comparative study with current algorithms was performed to access the effectiveness of the proposed one. The first experiment results indicate that the proposed algorithm best preserves the lesion dermoscopic structure and skin tones features. The second experiment, which consisted of training a convolutional neural network model with the augmented dataset, indicates a more significant increase in accuracy by 15.69%, and 15.38% respectively for tanned, and brown skin categories. The model precision, recall, and F1-score have also been increased. The obtained results indicate that the proposed augmentation method is suitable for dermoscopic images and can be used as a solution to the lack of dark skin images in the dataset. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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16 pages, 7825 KiB  
Article
Attention Mechanism Guided Deep Regression Model for Acne Severity Grading
by Saeed Alzahrani, Baidaa Al-Bander and Waleed Al-Nuaimy
Computers 2022, 11(3), 31; https://doi.org/10.3390/computers11030031 - 23 Feb 2022
Cited by 10 | Viewed by 4553
Abstract
Acne vulgaris is the common form of acne that primarily affects adolescents, characterised by an eruption of inflammatory and/or non-inflammatory skin lesions. Accurate evaluation and severity grading of acne play a significant role in precise treatment for patients. Manual acne examination is typically [...] Read more.
Acne vulgaris is the common form of acne that primarily affects adolescents, characterised by an eruption of inflammatory and/or non-inflammatory skin lesions. Accurate evaluation and severity grading of acne play a significant role in precise treatment for patients. Manual acne examination is typically conducted by dermatologists through visual inspection of the patient skin and counting the number of acne lesions. However, this task costs time and requires excessive effort by dermatologists. This paper presents automated acne counting and severity grading method from facial images. To this end, we develop a multi-scale dilated fully convolutional regressor for density map generation integrated with an attention mechanism. The proposed fully convolutional regressor module adapts UNet with dilated convolution filters to systematically aggregate multi-scale contextual information for density maps generation. We incorporate an attention mechanism represented by prior knowledge of bounding boxes generated by Faster R-CNN into the regressor model. This attention mechanism guides the regressor model on where to look for the acne lesions by locating the most salient features related to the understudied acne lesions, therefore improving its robustness to diverse facial acne lesion distributions in sparse and dense regions. Finally, integrating over the generated density maps yields the count of acne lesions within an image, and subsequently the acne count indicates the level of acne severity. The obtained results demonstrate improved performance compared to the state-of-the-art methods in terms of regression and classification metrics. The developed computer-based diagnosis tool would greatly benefit and support automated acne lesion severity grading, significantly reducing the manual assessment and evaluation workload. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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14 pages, 16960 KiB  
Article
Brain Tumour Classification Using Noble Deep Learning Approach with Parametric Optimization through Metaheuristics Approaches
by Dillip Ranjan Nayak, Neelamadhab Padhy, Pradeep Kumar Mallick, Dilip Kumar Bagal and Sachin Kumar
Computers 2022, 11(1), 10; https://doi.org/10.3390/computers11010010 - 7 Jan 2022
Cited by 41 | Viewed by 5869 | Correction
Abstract
Deep learning has surged in popularity in recent years, notably in the domains of medical image processing, medical image analysis, and bioinformatics. In this study, we offer a completely autonomous brain tumour segmentation approach based on deep neural networks (DNNs). We describe a [...] Read more.
Deep learning has surged in popularity in recent years, notably in the domains of medical image processing, medical image analysis, and bioinformatics. In this study, we offer a completely autonomous brain tumour segmentation approach based on deep neural networks (DNNs). We describe a unique CNN architecture which varies from those usually used in computer vision. The classification of tumour cells is very difficult due to their heterogeneous nature. From a visual learning and brain tumour recognition point of view, a convolutional neural network (CNN) is the most extensively used machine learning algorithm. This paper presents a CNN model along with parametric optimization approaches for analysing brain tumour magnetic resonance images. The accuracy percentage in the simulation of the above-mentioned model is exactly 100% throughout the nine runs, i.e., Taguchi’s L9 design of experiment. This comparative analysis of all three algorithms will pique the interest of readers who are interested in applying these techniques to a variety of technical and medical challenges. In this work, the authors have tuned the parameters of the convolutional neural network approach, which is applied to the dataset of Brain MRIs to detect any portion of a tumour, through new advanced optimization techniques, i.e., SFOA, FBIA and MGA. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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13 pages, 2049 KiB  
Article
Melanoma Detection in Dermoscopic Images Using a Cellular Automata Classifier
by Benjamín Luna-Benoso, José Cruz Martínez-Perales, Jorge Cortés-Galicia, Rolando Flores-Carapia and Víctor Manuel Silva-García
Computers 2022, 11(1), 8; https://doi.org/10.3390/computers11010008 - 4 Jan 2022
Cited by 7 | Viewed by 2895
Abstract
Any cancer type is one of the leading death causes around the world. Skin cancer is a condition where malignant cells are formed in the tissues of the skin, such as melanoma, known as the most aggressive and deadly skin cancer type. The [...] Read more.
Any cancer type is one of the leading death causes around the world. Skin cancer is a condition where malignant cells are formed in the tissues of the skin, such as melanoma, known as the most aggressive and deadly skin cancer type. The mortality rates of melanoma are associated with its high potential for metastasis in later stages, spreading to other body sites such as the lungs, bones, or the brain. Thus, early detection and diagnosis are closely related to survival rates. Computer Aided Design (CAD) systems carry out a pre-diagnosis of a skin lesion based on clinical criteria or global patterns associated with its structure. A CAD system is essentially composed by three modules: (i) lesion segmentation, (ii) feature extraction, and (iii) classification. In this work, a methodology is proposed for a CAD system development that detects global patterns using texture descriptors based on statistical measurements that allow melanoma detection from dermoscopic images. Image analysis was carried out using spatial domain methods, statistical measurements were used for feature extraction, and a classifier based on cellular automata (ACA) was used for classification. The proposed model was applied to dermoscopic images obtained from the PH2 database, and it was compared with other models using accuracy, sensitivity, and specificity as metrics. With the proposed model, values of 0.978, 0.944, and 0.987 of accuracy, sensitivity and specificity, respectively, were obtained. The results of the evaluated metrics show that the proposed method is more effective than other state-of-the-art methods for melanoma detection in dermoscopic images. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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17 pages, 8197 KiB  
Article
Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture
by Anuja Arora, Ambikesh Jayal, Mayank Gupta, Prakhar Mittal and Suresh Chandra Satapathy
Computers 2021, 10(11), 139; https://doi.org/10.3390/computers10110139 - 28 Oct 2021
Cited by 28 | Viewed by 9732
Abstract
Brain tumor segmentation seeks to separate healthy tissue from tumorous regions. This is an essential step in diagnosis and treatment planning to maximize the likelihood of successful treatment. Magnetic resonance imaging (MRI) provides detailed information about brain tumor anatomy, making it an important [...] Read more.
Brain tumor segmentation seeks to separate healthy tissue from tumorous regions. This is an essential step in diagnosis and treatment planning to maximize the likelihood of successful treatment. Magnetic resonance imaging (MRI) provides detailed information about brain tumor anatomy, making it an important tool for effective diagnosis which is requisite to replace the existing manual detection system where patients rely on the skills and expertise of a human. In order to solve this problem, a brain tumor segmentation & detection system is proposed where experiments are tested on the collected BraTS 2018 dataset. This dataset contains four different MRI modalities for each patient as T1, T2, T1Gd, and FLAIR, and as an outcome, a segmented image and ground truth of tumor segmentation, i.e., class label, is provided. A fully automatic methodology to handle the task of segmentation of gliomas in pre-operative MRI scans is developed using a U-Net-based deep learning model. The first step is to transform input image data, which is further processed through various techniques—subset division, narrow object region, category brain slicing, watershed algorithm, and feature scaling was done. All these steps are implied before entering data into the U-Net Deep learning model. The U-Net Deep learning model is used to perform pixel label segmentation on the segment tumor region. The algorithm reached high-performance accuracy on the BraTS 2018 training, validation, as well as testing dataset. The proposed model achieved a dice coefficient of 0.9815, 0.9844, 0.9804, and 0.9954 on the testing dataset for sets HGG-1, HGG-2, HGG-3, and LGG-1, respectively. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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18 pages, 2561 KiB  
Article
B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets
by Mohammad H. Nadimi-Shahraki, Mahdis Banaie-Dezfouli, Hoda Zamani, Shokooh Taghian and Seyedali Mirjalili
Computers 2021, 10(11), 136; https://doi.org/10.3390/computers10110136 - 25 Oct 2021
Cited by 103 | Viewed by 4817
Abstract
Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle this challenge, many metaheuristic algorithms are used to select [...] Read more.
Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle this challenge, many metaheuristic algorithms are used to select effective features. However, most of them are not effective and scalable enough to select effective features from large medical datasets as well as small ones. Therefore, in this paper, a binary moth-flame optimization (B-MFO) is proposed to select effective features from small and large medical datasets. Three categories of B-MFO were developed using S-shaped, V-shaped, and U-shaped transfer functions to convert the canonical MFO from continuous to binary. These categories of B-MFO were evaluated on seven medical datasets and the results were compared with four well-known binary metaheuristic optimization algorithms: BPSO, bGWO, BDA, and BSSA. In addition, the convergence behavior of the B-MFO and comparative algorithms were assessed, and the results were statistically analyzed using the Friedman test. The experimental results demonstrate a superior performance of B-MFO in solving the feature selection problem for different medical datasets compared to other comparative algorithms. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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Review

Jump to: Editorial, Research

19 pages, 1611 KiB  
Review
Robustly Effective Approaches on Motor Imagery-Based Brain Computer Interfaces
by Seraphim S. Moumgiakmas and George A. Papakostas
Computers 2022, 11(5), 61; https://doi.org/10.3390/computers11050061 - 24 Apr 2022
Cited by 7 | Viewed by 3542
Abstract
Motor Imagery Brain Computer Interfaces (MI-BCIs) are systems that receive the users’ brain activity as an input signal in order to communicate between the brain and the interface or an action to be performed through the detection of the imagination of a movement. [...] Read more.
Motor Imagery Brain Computer Interfaces (MI-BCIs) are systems that receive the users’ brain activity as an input signal in order to communicate between the brain and the interface or an action to be performed through the detection of the imagination of a movement. Brainwaves’ features are crucial for the performance of the interface to be increased. The robustness of these features must be ensured in order for the effectiveness to remain high in various subjects. The present work consists of a review, which includes scientific publications related to the use of robust feature extraction methods in Motor Imagery from 2017 until today. The research showed that the majority of the works focus on spatial features through Common Spatial Patterns (CSP) methods (44.26%). Based on the combination of accuracy percentages and K-values, which show the effectiveness of each approach, Wavelet Transform (WT) has shown higher robustness than CSP and PSD methods in the majority of the datasets used for comparison and also in the majority of the works included in the present review, although they had a lower usage percentage in the literature (16.65%). The research showed that there was an increase in 2019 of the detection of spatial features to increase the robustness of an approach, but the time-frequency features, or a combination of those, achieve better results with their increase starting from 2019 onwards. Additionally, Wavelet Transforms and their variants, in combination with deep learning, manage to achieve high percentages thus making a method robustly accurate. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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21 pages, 1579 KiB  
Review
A Review of Intelligent Sensor-Based Systems for Pressure Ulcer Prevention
by Arlindo Silva, José Metrôlho, Fernando Ribeiro, Filipe Fidalgo, Osvaldo Santos and Rogério Dionisio
Computers 2022, 11(1), 6; https://doi.org/10.3390/computers11010006 - 31 Dec 2021
Cited by 16 | Viewed by 8424
Abstract
Pressure ulcers are a critical issue not only for patients, decreasing their quality of life, but also for healthcare professionals, contributing to burnout from continuous monitoring, with a consequent increase in healthcare costs. Due to the relevance of this problem, many hardware and [...] Read more.
Pressure ulcers are a critical issue not only for patients, decreasing their quality of life, but also for healthcare professionals, contributing to burnout from continuous monitoring, with a consequent increase in healthcare costs. Due to the relevance of this problem, many hardware and software approaches have been proposed to ameliorate some aspects of pressure ulcer prevention and monitoring. In this article, we focus on reviewing solutions that use sensor-based data, possibly in combination with other intrinsic or extrinsic information, processed by some form of intelligent algorithm, to provide healthcare professionals with knowledge that improves the decision-making process when dealing with a patient at risk of developing pressure ulcers. We used a systematic approach to select 21 studies that were thoroughly reviewed and summarized, considering which sensors and algorithms were used, the most relevant data features, the recommendations provided, and the results obtained after deployment. This review allowed us not only to describe the state of the art regarding the previous items, but also to identify the three main stages where intelligent algorithms can bring meaningful improvement to pressure ulcer prevention and mitigation. Finally, as a result of this review and following discussion, we drew guidelines for a general architecture of an intelligent pressure ulcer prevention system. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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