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Diagnostics
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Classifications of Diseases Using Machine Learning Algorithms
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Classifications of Diseases Using Machine Learning Algorithms

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 20629

Special Issue Editors

Dr. Ozal Yildirim

E-Mail Website
Guest Editor
Department of Software Engineering, Firat University, Elazig, Turkey
Interests: deep learning; ECG analysis; medical image processing; signal processing
Prof. Dr. Derya Avci

E-Mail Website
Guest Editor
Department of Software Engineering, Firat University, Elazig, Turkey
Interests: pattern recognition; image processing; intelligent systems; artificial intelligence systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical diagnosis is the process of determining the cause of a disease or injury, and it is an essential part of modern healthcare. The ability to accurately diagnose a patient's condition is crucial for the effective treatment and management of disease. A diagnosis of a disease is generally made through the use of physical examination, laboratory tests, imaging tests, or other diagnostic methods.

Classification of diseases using machine learning algorithms is an active area of research in the field of medical informatics. With the increasing amount of medical data being generated, machine learning algorithms have the potential to assist physicians and researchers in identifying patterns and making more accurate diagnoses.

In this Special Issue, we aim to publish a collection of studies of machine learning algorithms in the classification of diseases using physiological signals and medical images. The medical images of various organs of different X-ray modalities, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), etc., can be employed for disease detection. We hope to publish many original machine and deep learning papers applied to medicine to improve the quality of decision making.

In this Special Issue, we focus on the classification of diseases using machine learning algorithms. We welcome high-quality, original foundations, research, reviews, and case reports.

Dr. Ozal Yildirim
Prof. Dr. Derya Avci
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. Diagnostics is an international peer-reviewed open access semimonthly 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 2600 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.

Keywords

  • artificial intelligence
  • classification of diseases
  • machine learning
  • medical image processing
  • deep learning
  • neural network

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Published Papers (6 papers)

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Research

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15 pages, 20853 KiB  
Article
MedKnee: A New Deep Learning-Based Software for Automated Prediction of Radiographic Knee Osteoarthritis
by Said Touahema, Imane Zaimi, Nabila Zrira, Mohamed Nabil Ngote, Hassan Doulhousne and Mohsine Aouial
Diagnostics 2024, 14(10), 993; https://doi.org/10.3390/diagnostics14100993 - 10 May 2024
Cited by 2 | Viewed by 1646
Abstract
In computer-aided medical diagnosis, deep learning techniques have shown that it is possible to offer performance similar to that of experienced medical specialists in the diagnosis of knee osteoarthritis. In this study, a new deep learning (DL) software, called “MedKnee” is developed to [...] Read more.
In computer-aided medical diagnosis, deep learning techniques have shown that it is possible to offer performance similar to that of experienced medical specialists in the diagnosis of knee osteoarthritis. In this study, a new deep learning (DL) software, called “MedKnee” is developed to assist physicians in the diagnosis process of knee osteoarthritis according to the Kellgren and Lawrence (KL) score. To accomplish this task, 5000 knee X-ray images obtained from the Osteoarthritis Initiative public dataset (OAI) were divided into train, valid, and test datasets in a ratio of 7:1:2 with a balanced distribution across each KL grade. The pre-trained Xception model is used for transfer learning and then deployed in a Graphical User Interface (GUI) developed with Tkinter and Python. The suggested software was validated on an external public database, Medical Expert, and compared with a rheumatologist’s diagnosis on a local database, with the involvement of a radiologist for arbitration. The MedKnee achieved an accuracy of 95.36% when tested on Medical Expert-I and 94.94% on Medical Expert-II. In the local dataset, the developed tool and the rheumatologist agreed on 23 images out of 30 images (74%). The MedKnee’s satisfactory performance makes it an effective assistant for doctors in the assessment of knee osteoarthritis. Full article
(This article belongs to the Special Issue Classifications of Diseases Using Machine Learning Algorithms)
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20 pages, 2359 KiB  
Article
A Multi-Stage Approach for Cardiovascular Risk Assessment from Retinal Images Using an Amalgamation of Deep Learning and Computer Vision Techniques
by Deepthi K. Prasad, Madhura Prakash Manjunath, Meghna S. Kulkarni, Spoorthi Kullambettu, Venkatakrishnan Srinivasan, Madhulika Chakravarthi and Anusha Ramesh
Diagnostics 2024, 14(9), 928; https://doi.org/10.3390/diagnostics14090928 - 29 Apr 2024
Cited by 1 | Viewed by 2114
Abstract
Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Early detection and effective risk assessment are crucial for implementing preventive measures and improving patient outcomes for CVDs. This work presents a novel approach to CVD risk assessment using fundus images, leveraging the [...] Read more.
Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Early detection and effective risk assessment are crucial for implementing preventive measures and improving patient outcomes for CVDs. This work presents a novel approach to CVD risk assessment using fundus images, leveraging the inherent connection between retinal microvascular changes and systemic vascular health. This study aims to develop a predictive model for the early detection of CVDs by evaluating retinal vascular parameters. This methodology integrates both handcrafted features derived through mathematical computation and retinal vascular patterns extracted by artificial intelligence (AI) models. By combining these approaches, we seek to enhance the accuracy and reliability of CVD risk prediction in individuals. The methodology integrates state-of-the-art computer vision algorithms and AI techniques in a multi-stage architecture to extract relevant features from retinal fundus images. These features encompass a range of vascular parameters, including vessel caliber, tortuosity, and branching patterns. Additionally, a deep learning (DL)-based binary classification model is incorporated to enhance predictive accuracy. A dataset comprising fundus images and comprehensive metadata from the clinical trials conducted is utilized for training and validation. The proposed approach demonstrates promising results in the early prediction of CVD risk factors. The interpretability of the approach is enhanced through visualization techniques that highlight the regions of interest within the fundus images that are contributing to the risk predictions. Furthermore, the validation conducted in the clinical trials and the performance analysis of the proposed approach shows the potential to provide early and accurate predictions. The proposed system not only aids in risk stratification but also serves as a valuable tool for identifying vascular abnormalities that may precede overt cardiovascular events. The approach has achieved an accuracy of 85% and the findings of this study underscore the feasibility and efficacy of leveraging fundus images for cardiovascular risk assessment. As a non-invasive and cost-effective modality, fundus image analysis presents a scalable solution for population-wide screening programs. This research contributes to the evolving landscape of precision medicine by providing an innovative tool for proactive cardiovascular health management. Future work will focus on refining the solution’s robustness, exploring additional risk factors, and validating its performance in additional and diverse clinical settings. Full article
(This article belongs to the Special Issue Classifications of Diseases Using Machine Learning Algorithms)
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20 pages, 30289 KiB  
Article
Classification of Emotional and Immersive Outcomes in the Context of Virtual Reality Scene Interactions
by Yaşar Daşdemir
Diagnostics 2023, 13(22), 3437; https://doi.org/10.3390/diagnostics13223437 - 13 Nov 2023
Cited by 5 | Viewed by 1797
Abstract
The constantly evolving technological landscape of the Metaverse has introduced a significant concern: cybersickness (CS). There is growing academic interest in detecting and mitigating these adverse effects within virtual environments (VEs). However, the development of effective methodologies in this field has been hindered [...] Read more.
The constantly evolving technological landscape of the Metaverse has introduced a significant concern: cybersickness (CS). There is growing academic interest in detecting and mitigating these adverse effects within virtual environments (VEs). However, the development of effective methodologies in this field has been hindered by the lack of sufficient benchmark datasets. In pursuit of this objective, we meticulously compiled a comprehensive dataset by analyzing the impact of virtual reality (VR) environments on CS, immersion levels, and EEG-based emotion estimation. Our dataset encompasses both implicit and explicit measurements. Implicit measurements focus on brain signals, while explicit measurements are based on participant questionnaires. These measurements were used to collect data on the extent of cybersickness experienced by participants in VEs. Using statistical methods, we conducted a comparative analysis of CS levels in VEs tailored for specific tasks and their immersion factors. Our findings revealed statistically significant differences between VEs, highlighting crucial factors influencing participant engagement, engrossment, and immersion. Additionally, our study achieved a remarkable classification performance of 96.25% in distinguishing brain oscillations associated with VR scenes using the multi-instance learning method and 95.63% in predicting emotions within the valence-arousal space with four labels. The dataset presented in this study holds great promise for objectively evaluating CS in VR contexts, differentiating between VEs, and providing valuable insights for future research endeavors. Full article
(This article belongs to the Special Issue Classifications of Diseases Using Machine Learning Algorithms)
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15 pages, 2555 KiB  
Article
Early Diagnosis and Classification of Fetal Health Status from a Fetal Cardiotocography Dataset Using Ensemble Learning
by Adem Kuzu and Yunus Santur
Diagnostics 2023, 13(15), 2471; https://doi.org/10.3390/diagnostics13152471 - 25 Jul 2023
Cited by 7 | Viewed by 2643
Abstract
(1) Background: According to the World Health Organization (WHO), 6.3 million intrauterine fetal deaths occur every year. The most common method of diagnosing perinatal death and taking early precautions for maternal and fetal health is a nonstress test (NST). Data on the fetal [...] Read more.
(1) Background: According to the World Health Organization (WHO), 6.3 million intrauterine fetal deaths occur every year. The most common method of diagnosing perinatal death and taking early precautions for maternal and fetal health is a nonstress test (NST). Data on the fetal heart rate and uterus contractions from an NST device are interpreted based on a trace printer’s output, allowing for a diagnosis of fetal health to be made by an expert. (2) Methods: in this study, a predictive method based on ensemble learning is proposed for the classification of fetal health (normal, suspicious, pathology) using a cardiotocography dataset of fetal movements and fetal heart rate acceleration from NST tests. (3) Results: the proposed predictor achieved an accuracy level above 99.5% on the test dataset. (4) Conclusions: from the experimental results, it was observed that a fetal health diagnosis can be made during NST using machine learning. Full article
(This article belongs to the Special Issue Classifications of Diseases Using Machine Learning Algorithms)
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19 pages, 7996 KiB  
Article
An Explainable Vision Transformer Model Based White Blood Cells Classification and Localization
by Oguzhan Katar and Ozal Yildirim
Diagnostics 2023, 13(14), 2459; https://doi.org/10.3390/diagnostics13142459 - 24 Jul 2023
Cited by 6 | Viewed by 5701
Abstract
White blood cells (WBCs) are crucial components of the immune system that play a vital role in defending the body against infections and diseases. The identification of WBCs subtypes is useful in the detection of various diseases, such as infections, leukemia, and other [...] Read more.
White blood cells (WBCs) are crucial components of the immune system that play a vital role in defending the body against infections and diseases. The identification of WBCs subtypes is useful in the detection of various diseases, such as infections, leukemia, and other hematological malignancies. The manual screening of blood films is time-consuming and subjective, leading to inconsistencies and errors. Convolutional neural networks (CNN)-based models can automate such classification processes, but are incapable of capturing long-range dependencies and global context. This paper proposes an explainable Vision Transformer (ViT) model for automatic WBCs detection from blood films. The proposed model uses a self-attention mechanism to extract features from input images. Our proposed model was trained and validated on a public dataset of 16,633 samples containing five different types of WBCs. As a result of experiments on the classification of five different types of WBCs, our model achieved an accuracy of 99.40%. Moreover, the model’s examination of misclassified test samples revealed a correlation between incorrect predictions and the presence or absence of granules in the cell samples. To validate this observation, we divided the dataset into two classes, Granulocytes and Agranulocytes, and conducted a secondary training process. The resulting ViT model, trained for binary classification, achieved impressive performance metrics during the test phase, including an accuracy of 99.70%, recall of 99.54%, precision of 99.32%, and F-1 score of 99.43%. To ensure the reliability of the ViT model’s, we employed the Score-CAM algorithm to visualize the pixel areas on which the model focuses during its predictions. Our proposed method is suitable for clinical use due to its explainable structure as well as its superior performance compared to similar studies in the literature. The classification and localization of WBCs with this model can facilitate the detection and reporting process for the pathologist. Full article
(This article belongs to the Special Issue Classifications of Diseases Using Machine Learning Algorithms)
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Review

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21 pages, 1694 KiB  
Review
Skeletal Fracture Detection with Deep Learning: A Comprehensive Review
by Zhihao Su, Afzan Adam, Mohammad Faidzul Nasrudin, Masri Ayob and Gauthamen Punganan
Diagnostics 2023, 13(20), 3245; https://doi.org/10.3390/diagnostics13203245 - 18 Oct 2023
Cited by 9 | Viewed by 5598
Abstract
Deep learning models have shown great promise in diagnosing skeletal fractures from X-ray images. However, challenges remain that hinder progress in this field. Firstly, a lack of clear definitions for recognition, classification, detection, and localization tasks hampers the consistent development and comparison of [...] Read more.
Deep learning models have shown great promise in diagnosing skeletal fractures from X-ray images. However, challenges remain that hinder progress in this field. Firstly, a lack of clear definitions for recognition, classification, detection, and localization tasks hampers the consistent development and comparison of methodologies. The existing reviews often lack technical depth or have limited scope. Additionally, the absence of explainable facilities undermines the clinical application and expert confidence in results. To address these issues, this comprehensive review analyzes and evaluates 40 out of 337 recent papers identified in prestigious databases, including WOS, Scopus, and EI. The objectives of this review are threefold. Firstly, precise definitions are established for the bone fracture recognition, classification, detection, and localization tasks within deep learning. Secondly, each study is summarized based on key aspects such as the bones involved, research objectives, dataset sizes, methods employed, results obtained, and concluding remarks. This process distills the diverse approaches into a generalized processing framework or workflow. Moreover, this review identifies the crucial areas for future research in deep learning models for bone fracture diagnosis. These include enhancing the network interpretability, integrating multimodal clinical information, providing therapeutic schedule recommendations, and developing advanced visualization methods for clinical application. By addressing these challenges, deep learning models can be made more intelligent and specialized in this domain. In conclusion, this review fills the gap in precise task definitions within deep learning for bone fracture diagnosis and provides a comprehensive analysis of the recent research. The findings serve as a foundation for future advancements, enabling improved interpretability, multimodal integration, clinical decision support, and advanced visualization techniques. Full article
(This article belongs to the Special Issue Classifications of Diseases Using Machine Learning Algorithms)
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