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A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 10365

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Dr. Maryam Ravan

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Department of Electrical and Computer Engineering, New York Institute of Technology (NYIT), NYC Campus, Room 810, 1855 Broadway, New York, NY 10023-7692, USA
Interests: signal processing; machine learning; biomedical engineering; microwave imaging; non-destructive testing
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Dear Colleagues,

We invite you to submit your latest research focused on developing and applying machine learning algorithms for medical applications to this Special Issue, “Machine Learning in Medical Signal and Image Processing (2nd Edition)”. We are looking for new and innovative machine learning approaches with medical applications. Potential applications include, but are not limited to, biomedical signal processing, biomedical image processing, biosensors, bioinformatics and computational biology, neural and rehabilitation engineering, cardiovascular engineering, therapeutic and diagnostic systems, robotics, clinical engineering, healthcare information systems and telemedicine, devices and technologies, and emerging topics in biomedical engineering.

Dr. Maryam Ravan
Guest Editor

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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. Algorithms 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 1600 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
disease classification and prognosis prediction
deep learning (CNN, RNN, GAN, etc.) in brain–computer interface (BCI) and medical images
radiological image processing (MRI, fMRI, CT scan, PET, ultrasound, X-ray, etc.)
clinical data processing (electrocardiography (ECG), electromyography (EMG), electroencephalography (EEG), etc.)
data fusion techniques
statistical pattern recognition
advanced artifact reduction
wearable sensors
virtual reality

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Related Special Issue

Machine Learning in Medical Signal and Image Processing in Algorithms (11 articles)

Published Papers (8 papers)

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Research

26 pages, 862 KiB

Open AccessArticle

Can the Plantar Pressure and Temperature Data Trend Show the Presence of Diabetes? A Comparative Study of a Variety of Machine Learning Techniques

by Eduardo A. Gerlein, Francisco Calderón, Martha Zequera-Díaz and Roozbeh Naemi

Algorithms 2024, 17(11), 519; https://doi.org/10.3390/a17110519 - 12 Nov 2024

Viewed by 499

Abstract

This study aimed to explore the potential of predicting diabetes by analyzing trends in plantar thermal and plantar pressure data, either individually or in combination, using various machine learning techniques. A total of twenty-six participants, comprising thirteen individuals diagnosed with diabetes and thirteen healthy individuals, walked along a 20 m path. In-shoe plantar pressure data were collected and the plantar temperature was measured both immediately before and after the walk. Each participant completed the trial three times, and the average data between the trials were calculated. The research was divided into three experiments: the first evaluated the correlations between the plantar pressure and temperature data; the second focused on predicting diabetes using each data type independently; and the third combined both data types and assessed the effect of such to enhance the predictive accuracy. For the experiments, 20 regression models and 16 classification algorithms were employed, and the performance was evaluated using a five-fold cross-validation strategy. The outcomes of the initial set of experiments indicated that the machine learning models were significant correlations between the thermal data and pressure estimates. This was consistent with the findings from the prior correlation analysis, which showed weak relationships between these two data modalities. However, a shift in focus towards predicting diabetes by aggregating the temperature and pressure data led to encouraging results, demonstrating the effectiveness of this approach in accurately predicting the presence of diabetes. The analysis revealed that, while several classifiers demonstrated reasonable metrics when using standalone variables, the integration of thermal and pressure data significantly improved the predictive accuracy. Specifically, when only plantar pressure data were used, the Logistic Regression model achieved the highest accuracy at 68.75%. Those predictions based solely on temperature data showed the Naive Bayes model as the lead with an accuracy of 87.5%. Notably, the highest accuracy of 93.75% was observed when both the temperature and pressure data were combined, with the Extra Trees Classifier performing the best. These results suggest that combining temperature and pressure data enhances the model’s predictive accuracy. This can indicate the importance of multimodal data integration and their potentials in diabetes prediction. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (2nd Edition))

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19 pages, 1604 KiB

Open AccessArticle

Investigating Brain Responses to Transcutaneous Electroacupuncture Stimulation: A Deep Learning Approach

by Tahereh Vasei, Harshil Gediya, Maryam Ravan, Anand Santhanakrishnan, David Mayor and Tony Steffert

Algorithms 2024, 17(11), 477; https://doi.org/10.3390/a17110477 - 24 Oct 2024

Viewed by 658

Abstract

This study investigates the neurophysiological effects of transcutaneous electroacupuncture stimulation (TEAS) on brain activity, using advanced machine learning techniques. This work analyzed the electroencephalograms (EEG) of 48 study participants, in order to analyze the brain’s response to different TEAS frequencies (2.5, 10, 80, and sham at 160 pulses per second (pps)) across 48 participants through pre-stimulation, during-stimulation, and post-stimulation phases. Our approach introduced several novel aspects. EEGNet, a convolutional neural network specifically designed for EEG signal processing, was utilized in this work, achieving over 95% classification accuracy in detecting brain responses to various TEAS frequencies. Additionally, the classification accuracies across the pre-stimulation, during-stimulation, and post-stimulation phases remained consistently high (above 92%), indicating that EEGNet effectively captured the different time-based brain responses across different stimulation phases. Saliency maps were applied to identify the most critical EEG electrodes, potentially reducing the number needed without sacrificing accuracy. A phase-based analysis was conducted to capture time-based brain responses throughout different stimulation phases. The robustness of EEGNet was assessed across demographic and clinical factors, including sex, age, and psychological states. Additionally, the responsiveness of different EEG frequency bands to TEAS was investigated. The results demonstrated that EEGNet excels in classifying EEG signals with high accuracy, underscoring its effectiveness in reliably classifying EEG responses to TEAS and enhancing its applicability in clinical and therapeutic settings. Notably, gamma band activity showed the highest sensitivity to TEAS, suggesting significant effects on higher cognitive functions. Saliency mapping revealed that a subset of electrodes (Fp1, Fp2, Fz, F7, F8, T3, T4) could achieve accurate classification, indicating potential for more efficient EEG setups. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (2nd Edition))

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12 pages, 6087 KiB

Open AccessArticle

Detection of Subtle ECG Changes Despite Superimposed Artifacts by Different Machine Learning Algorithms

by Matthias Noitz, Christoph Mörtl, Carl Böck, Christoph Mahringer, Ulrich Bodenhofer, Martin W. Dünser and Jens Meier

Algorithms 2024, 17(8), 360; https://doi.org/10.3390/a17080360 - 16 Aug 2024

Viewed by 635

Abstract

Analyzing electrocardiographic (ECG) signals is crucial for evaluating heart function and diagnosing cardiac pathology. Traditional methods for detecting ECG changes often rely on offline analysis or subjective visual inspection, which may overlook subtle variations, particularly in the case of artifacts. In this theoretical, proof-of-concept study, we investigated the potential of five different machine learning algorithms [random forests (RFs), gradient boosting methods (GBMs), deep neural networks (DNNs), an ensemble learning technique, as well as logistic regression] to detect subtle changes in the morphology of synthetically generated ECG beats despite artifacts. Following the generation of a synthetic ECG beat using the standardized McSharry algorithm, the baseline ECG signal was modified by changing the amplitude of different ECG components by 0.01–0.06 mV. In addition, a Gaussian jitter of 0.1–0.3 mV was overlaid to simulate artifacts. Five different machine learning algorithms were then applied to detect differences between the modified ECG beats. The highest discriminatory potency, as assessed by the discriminatory accuracy, was achieved by RFs and GBMs (accuracy of up to 1.0), whereas the least accurate results were obtained by logistic regression (accuracy approximately 10% less). In a second step, a feature importance algorithm (Boruta) was used to discriminate which signal parts were responsible for difference detection. For all comparisons, only signal components that had been modified in advance were used for discretion, demonstrating that the RF model focused on the appropriate signal elements. Our findings highlight the potential of RFs and GBMs as valuable tools for detecting subtle ECG changes despite artifacts, with implications for enhancing clinical diagnosis and monitoring. Further studies are needed to validate our findings with clinical data. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (2nd Edition))

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17 pages, 8135 KiB

Open AccessArticle

LungVision: X-ray Imagery Classification for On-Edge Diagnosis Applications

by Raghad Aldamani, Diaa Addeen Abuhani and Tamer Shanableh

Algorithms 2024, 17(7), 280; https://doi.org/10.3390/a17070280 - 27 Jun 2024

Cited by 1 | Viewed by 1016

Abstract

This study presents a comprehensive analysis of utilizing TensorFlow Lite on mobile phones for the on-edge medical diagnosis of lung diseases. This paper focuses on the technical deployment of various deep learning architectures to classify nine respiratory system diseases using X-ray imagery. We propose a simple deep learning architecture that experiments with six different convolutional neural networks. Various quantization techniques are employed to convert the classification models into TensorFlow Lite, including post-classification quantization with floating point 16 bit representation, integer quantization with representative data, and quantization-aware training. This results in a total of 18 models suitable for on-edge deployment for the classification of lung diseases. We then examine the generated models in terms of model size reduction, accuracy, and inference time. Our findings indicate that the quantization-aware training approach demonstrates superior optimization results, achieving an average model size reduction of 75.59%. Among many CNNs, MobileNetV2 exhibited the highest performance-to-size ratio, with an average accuracy loss of 4.1% across all models using the quantization-aware training approach. In terms of inference time, TensorFlow Lite with integer quantization emerged as the most efficient technique, with an average improvement of 1.4 s over other conversion approaches. Our best model, which used EfficientNetB2, achieved an F1-Score of approximately 98.58%, surpassing state-of-the-art performance on the X-ray lung diseases dataset in terms of accuracy, specificity, and sensitivity. The model experienced an F1 loss of around 1% using quantization-aware optimization. The study culminated in the development of a consumer-ready app, with TensorFlow Lite models tailored to mobile devices. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (2nd Edition))

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15 pages, 1574 KiB

Open AccessArticle

Exploring Data Augmentation Algorithm to Improve Genomic Prediction of Top-Ranking Cultivars

by Osval A. Montesinos-López, Arvinth Sivakumar, Gloria Isabel Huerta Prado, Josafhat Salinas-Ruiz, Afolabi Agbona, Axel Efraín Ortiz Reyes, Khalid Alnowibet, Rodomiro Ortiz, Abelardo Montesinos-López and José Crossa

Algorithms 2024, 17(6), 260; https://doi.org/10.3390/a17060260 - 14 Jun 2024

Cited by 1 | Viewed by 2619

Abstract

Genomic selection (GS) is a groundbreaking statistical machine learning method for advancing plant and animal breeding. Nonetheless, its practical implementation remains challenging due to numerous factors affecting its predictive performance. This research explores the potential of data augmentation to enhance prediction accuracy across entire datasets and specifically within the top 20% of the testing set. Our findings indicate that, overall, the data augmentation method (method A), when compared to the conventional model (method C) and assessed using Mean Arctangent Absolute Prediction Error (MAAPE) and normalized root mean square error (NRMSE), did not improve the prediction accuracy for the unobserved cultivars. However, significant improvements in prediction accuracy (evidenced by reduced prediction error) were observed when data augmentation was applied exclusively to the top 20% of the testing set. Specifically, reductions in MAAPE_20 and NRMSE_20 by 52.86% and 41.05%, respectively, were noted across various datasets. Further investigation is needed to refine data augmentation techniques for effective use in genomic prediction. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (2nd Edition))

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16 pages, 4411 KiB

Open AccessArticle

Employing a Convolutional Neural Network to Classify Sleep Stages from EEG Signals Using Feature Reduction Techniques

by Maadh Rajaa Mohammed and Ali Makki Sagheer

Algorithms 2024, 17(6), 229; https://doi.org/10.3390/a17060229 - 24 May 2024

Viewed by 915

Abstract

One of the most essential components of human life is sleep. One of the first steps in spotting abnormalities connected to sleep is classifying sleep stages. Based on the kind and frequency of signals obtained during a polysomnography test, sleep phases can be separated into groups. Accurate classification of sleep stages from electroencephalogram (EEG) signals plays a crucial role in sleep disorder diagnosis and treatment. This study proposes a novel approach that combines feature selection techniques with convolutional neural networks (CNNs) to enhance the classification performance of sleep stages using EEG signals. Firstly, a comprehensive feature selection process was employed to extract discriminative features from raw EEG data, aiming to reduce dimensionality and enhance the efficiency of subsequent classification using mutual information (MI) and analysis of variance (ANOVA) after splitting the dataset into two sets—the training set (70%) and testing set (30%)—then processing it using the standard scalar method. Subsequently, a 1D-CNN architecture was designed to automatically learn hierarchical representations of the selected features, capturing complex patterns indicative of different sleep stages. The proposed method was evaluated on a publicly available EDF-Sleep dataset, demonstrating superior performance compared to traditional approaches. The results highlight the effectiveness of integrating feature selection with CNNs in improving the accuracy and reliability of sleep stage classification from EEG signals, which reached 99.84% with MI-50. This approach not only contributes to advancing the field of sleep disorder diagnosis, but also holds promise for developing more efficient and robust clinical decision support systems. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (2nd Edition))

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17 pages, 2655 KiB

Open AccessArticle

Spike-Weighted Spiking Neural Network with Spiking Long Short-Term Memory: A Biomimetic Approach to Decoding Brain Signals

by Kyle McMillan, Rosa Qiyue So, Camilo Libedinsky, Kai Keng Ang and Brian Premchand

Algorithms 2024, 17(4), 156; https://doi.org/10.3390/a17040156 - 12 Apr 2024

Viewed by 1787

Abstract

Background. Brain–machine interfaces (BMIs) offer users the ability to directly communicate with digital devices through neural signals decoded with machine learning (ML)-based algorithms. Spiking Neural Networks (SNNs) are a type of Artificial Neural Network (ANN) that operate on neural spikes instead of continuous scalar outputs. Compared to traditional ANNs, SNNs perform fewer computations, use less memory, and mimic biological neurons better. However, SNNs only retain information for short durations, limiting their ability to capture long-term dependencies in time-variant data. Here, we propose a novel spike-weighted SNN with spiking long short-term memory (swSNN-SLSTM) for a regression problem. Spike-weighting captures neuronal firing rate instead of membrane potential, and the SLSTM layer captures long-term dependencies. Methods. We compared the performance of various ML algorithms during decoding directional movements, using a dataset of microelectrode recordings from a macaque during a directional joystick task, and also an open-source dataset. We thus quantified how swSNN-SLSTM performed compared to existing ML models: an unscented Kalman filter, LSTM-based ANN, and membrane-based SNN techniques. Result. The proposed swSNN-SLSTM outperforms both the unscented Kalman filter, the LSTM-based ANN, and the membrane based SNN technique. This shows that incorporating SLSTM can better capture long-term dependencies within neural data. Also, our proposed swSNN-SLSTM algorithm shows promise in reducing power consumption and lowering heat dissipation in implanted BMIs. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (2nd Edition))

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12 pages, 941 KiB

Open AccessArticle

Challenges in Reducing Bias Using Post-Processing Fairness for Breast Cancer Stage Classification with Deep Learning

by Armin Soltan and Peter Washington

Algorithms 2024, 17(4), 141; https://doi.org/10.3390/a17040141 - 28 Mar 2024

Viewed by 1428

Abstract

Breast cancer is the most common cancer affecting women globally. Despite the significant impact of deep learning models on breast cancer diagnosis and treatment, achieving fairness or equitable outcomes across diverse populations remains a challenge when some demographic groups are underrepresented in the training data. We quantified the bias of models trained to predict breast cancer stage from a dataset consisting of 1000 biopsies from 842 patients provided by AIM-Ahead (Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity). Notably, the majority of data (over 70%) were from White patients. We found that prior to post-processing adjustments, all deep learning models we trained consistently performed better for White patients than for non-White patients. After model calibration, we observed mixed results, with only some models demonstrating improved performance. This work provides a case study of bias in breast cancer medical imaging models and highlights the challenges in using post-processing to attempt to achieve fairness. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (2nd Edition))

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