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Machine Learning in Medical Signal and Image Processing (4th Edition)

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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 (28 February 2026) | Viewed by 12941

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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 on the development and medical applications of machine learning algorithms to this Special Issue, “Machine Learning in Medical Signal and Image Processing (4th Edition)”.

We are looking for papers showcasing new, innovative machine learning approaches with medical applications, including, but not limited to, the following: biomedical signal and image processing; biosensors; bioinformatics and computational biology; neural, rehabilitation, cardiovascular, and clinical engineering; therapeutic and diagnostic systems; robotics; healthcare information systems and telemedicine; devices and technologies; and emerging topics in biomedical engineering.

Dr. Maryam Ravan
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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 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.

Keywords

artificial intelligence
disease classification and prognosis prediction
deep learning (CNN, RNN, GAN, etc.) in brain–computer interfaces (BCIs) and medical images
radiological image processing (MRI, fMRI, CT scans, 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 (3rd Edition) in Algorithms (12 articles)

Published Papers (9 papers)

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Research

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29 pages, 8841 KB

Open AccessArticle

Virtual Reality Interventions for Enhancing Executive Functions in Children and Adolescents with Autism Spectrum Disorder

by Angeliki Sideraki and Christos-Nikolaos Anagnostopoulos

Algorithms 2026, 19(3), 201; https://doi.org/10.3390/a19030201 - 7 Mar 2026

Viewed by 621

Abstract

This study investigates the impact of a Virtual Reality (VR)-based intervention on the enhancement of executive functions—cognitive flexibility, inhibitory control, and working memory—in children diagnosed with Autism Spectrum Disorder (ASD). Employing a single-case experimental design with repeated measures, the research was conducted with two male participants, aged 9 and 10, both formally diagnosed with ASD. The intervention was structured into four phases: Baseline (no training), Intervention (targeted VR training), Generalization (skill transfer testing), and Follow-up (maintenance assessment). Each participant engaged in a total of 18 tasks (six per executive function), delivered through immersive VR environments featuring gamified elements, adaptive feedback, and increasing difficulty. Each task consisted of up to 15 sub-items, scored as correct or incorrect. Results indicate consistent improvements across executive function domains during the intervention phase, with partial maintenance at follow-up and evidence of task generalization. Given the single-case framework and limited sample size, findings should be interpreted as exploratory and hypothesis-generating rather than population-generalizable. The study provides proof-of-concept evidence supporting the feasibility and potential of immersive VR-based executive function training for ASD populations, warranting further validation through larger-scale controlled trials. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (4th Edition))

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14 pages, 1506 KB

Open AccessArticle

LightGBM-Based Seizure Detection Method in Pilocarpine Mouse Model of Epilepsy

by Mercy Edoho, Nicolas Partouche, Christiaan Warner Hoornenborg, Tycho M. Hoogland, Stéphane Baudouin, Catherine Mooney and Lan Wei

Algorithms 2026, 19(3), 167; https://doi.org/10.3390/a19030167 - 24 Feb 2026

Viewed by 462

Abstract

Electroencephalogram (EEG) has been the gold standard for measuring epileptic activity in rodent models of epilepsy. Manual scoring of seizures in EEG recordings lasting from days to months is laborious and prone to human error. The existing literature on automatic seizure detection in rodent models of epilepsy is limited, and the electrographic characteristics of induced epilepsy significantly differ from those of other epilepsy types. This study employed a Light Gradient Boosting Machine (LightGBM), with the dataset carefully partitioned into separate training and testing sets to ensure no data overlap. The model was trained using five-fold cross-validation to enhance robustness and generalisability. The training, validation, and independent test sets comprised 29,722 h of EEG recordings from 102 mice with pilocarpine-induced temporal lobe epilepsy. Following feature selection, model training, and post-processing, the lightGBM-based model exhibited a sensitivity of 80%, a specificity of 99%, and an F1-score of 0.71 on the independent test set. Multiple pairwise and non-parametric statistical tests indicated that envelope, skewness, and kurtosis, identified as the three most significant features in the feature importance ranking, exhibit statistically significant differences in their distributions (p-value < 0.05). The statistical analysis revealed significant differences across the three features and between seizure and non-seizure events for each feature, highlighting their relevance for discriminating epileptic activity. This study highlights the potential to support the automation of seizure event detection in preclinical rodent models of epilepsy. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (4th Edition))

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26 pages, 2808 KB

Open AccessArticle

An Automated ECG-PCG Coupling Analysis System with LLM-Assisted Semantic Reporting for Community and Home-Based Cardiac Monitoring

by Yi Tang, Fei Cong, Yi Li and Ping Shi

Algorithms 2026, 19(2), 117; https://doi.org/10.3390/a19020117 - 2 Feb 2026

Viewed by 585

Abstract

Objective: Cardiac monitoring in community and home environments requires automated operation, cross-state robustness, and interpretable feedback under resource-constrained and uncontrolled conditions. Unlike accuracy-driven ECG–PCG studies focusing on diagnostic performance, this work emphasizes systematic modeling of cardiac electromechanical coupling for long-term monitoring and engineering feasibility validation. Methods: An automated ECG–PCG coupling analysis and semantic reporting framework is proposed, covering signal preprocessing, event detection and calibration, multimodal coupling feature construction, and rule-constrained LLM-assisted interpretation. Electrical events from ECG are used as global temporal references, while multi-stage consistency correction mechanisms are introduced to enhance the stability of mechanical event localization under noise and motion interference. A structured electromechanical feature set is constructed to support fully automated processing. Results: Experimental results demonstrate that the proposed system maintains coherent event sequences and stable coupling parameter extraction across resting, movement, and emotional stress conditions. The incorporated LLM module integrates precomputed multimodal metrics under strict constraints, improving report readability and consistency without performing autonomous medical interpretation. Conclusions: This study demonstrates the methodological feasibility of an ECG–PCG coupling analysis framework for long-term cardiac state monitoring in low-resource environments. By integrating end-to-end automation, electromechanical coupling features, and constrained semantic reporting, the proposed system provides an engineering-oriented reference for continuous cardiac monitoring in community and home settings rather than a clinical diagnostic solution. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (4th Edition))

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22 pages, 3886 KB

Open AccessArticle

Impact of Image Enhancement Using Contrast-Limited Adaptive Histogram Equalization (CLAHE), Anisotropic Diffusion, and Histogram Equalization on Spine X-Ray Segmentation with U-Net, Mask R-CNN, and Transfer Learning

by Muhammad Shahrul Zaim Ahmad, Nor Azlina Ab. Aziz, Heng Siong Lim, Anith Khairunnisa Ghazali and ‘Afif Abdul Latiff

Algorithms 2025, 18(12), 796; https://doi.org/10.3390/a18120796 - 16 Dec 2025

Cited by 4 | Viewed by 2194

Abstract

Image segmentation is one of the important applications of deep learning models, such as U-Net and Mask R-CNN, in medical imaging. The image segmentation process enables automated extraction of important information within images, including spine X-rays, saving medical practitioners hours of work. However, for X-ray images, low contrast and noise may affect the quality of the images and consequently reduce the effectiveness of the deep learning models in providing a robust segmentation. Image enhancement prior to feeding the images to segmentation models can help to overcome the issues caused by the low-quality images. This paper aims to evaluate the effects of three image enhancement methods, namely, the contrast-limited adaptive histogram equalization (CLAHE), histogram equalization (HE), and anisotropic diffusion (AD), for improving image segmentation performance of Mask R-CNN, non-transfer learning Mask R-CNN, and U-Net. The findings show image enhancement methods provide significant improvement to the U-Net, and, interestingly, no noticeable improvement of performance on Mask R-CNN is observed. The application of HE for transfer learning Mask R-CNN achieved the highest Dice score of 0.942 ± 0.001 for binary segmentation. The randomly initialized Mask R-CNN obtains the highest DSC of 0.941 ± 0.002 on the same task. On the other hand, for U-Net, despite the presence of statistically significant change by applying image enhancement methods, the model achieves a maximum Dice score of 0.916 ± 0.003, lower than Mask R-CNN with and without transfer learning. A study on image enhancement methods and recent deep learning algorithms is necessary to better understand the effect of image enhancement techniques using deep learning. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (4th Edition))

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25 pages, 7527 KB

Open AccessArticle

A Multifocal RSSeg Approach for Skeletal Age Estimation in an Indian Medicolegal Perspective

by Priyanka Manchegowda, Manohar Nageshmurthy, Suresha Raju and Dayananda Rudrappa

Algorithms 2025, 18(12), 765; https://doi.org/10.3390/a18120765 - 4 Dec 2025

Cited by 1 | Viewed by 793

Abstract

Estimating bone age is essential for accurate diagnoses, appropriate care based on biological age, and fairness in legal matters. In the Indian medicolegal context, determining age through a clinical approach involves analyzing multiple joints; however, the traditional method can be tedious and subjective, relying heavily on human expertise, which may lead to biased decisions in age-related legal disputes. Moreover, commonly used radiographs often exhibit pixel-level variations due to heterogeneous contrast, which complicate segmentation tasks and lead to inconsistencies and reduced model performance. The study presents a multifocal region-based symbolic segmentation technique to automatically retain the soft-tissue region that harbors a growth pattern of an ossification center. Experimental results demonstrate an 84.5% Jaccard similarity, an 81.4% Dice coefficient, an 88.3% precision, a 90.0% recall, and a 91.5% pixel accuracy on a novel multifocal dataset of Indian inhabitants. The proposed segmentation technique outperforms U-Net, Attention U-Net, TransU-Net, DeepLabV3+, Adaptive Otsu, and Watershed segmentation in terms of accuracy, indicating strong generalizability across joints and improving reliability. Compared with 86.4% without segmentation, the proposed integration of segmentation with VGG16 classification increases the overall accuracy to 93.8%, demonstrating that target-focused-region processing reduces unnecessary computations and improves feature discrimination without sacrificing accuracy. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (4th Edition))

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23 pages, 1986 KB

Open AccessArticle

GMHCA-MCBILSTM: A Gated Multi-Head Cross-Modal Attention-Based Network for Emotion Recognition Using Multi-Physiological Signals

by Xueping Li, Yanbo Li, Yuhang Li and Yuan Yang

Algorithms 2025, 18(10), 664; https://doi.org/10.3390/a18100664 - 20 Oct 2025

Cited by 1 | Viewed by 1600

Abstract

To address the limitations of the single-modal electroencephalogram (EEG), such as its single physiological dimension, weak anti-interference ability, and inability to fully reflect emotional states, this paper proposes a gated multi-head cross-attention module (GMHCA) for multimodal fusion of EEG, electrooculography (EOG),and electrodermal activity (EDA). This attention module employs three independent and parallel attention computation units to assign independent attention weights to different feature subsets across modalities. Combined with a modality complementarity metric, the gating mechanism suppresses redundant heads and enhances the information transmission of key heads. Through multi-head concatenation, cross-modal interaction results from different perspectives are fused. For the backbone network, a multi-scale convolution and bidirectional long short-term memory network (MC-BiLSTM) is designed for feature extraction, tailored to the characteristics of each modality. Experiments show that this method, which primarily fuses eight-channel EEG with peripheral physiological signals, achieves an emotion recognition accuracy of 89.45%, a 7.68% improvement over single-modal EEG. In addition, in cross-subject experiments conducted on the SEED-IV dataset, the EEG+EOG modality achieved a classification accuracy of 92.73%. All were significantly better than the baseline method. This fully demonstrates the effectiveness of the innovative GMHCA module architecture and MC-BiLSTM feature extraction network proposed in this paper for multimodal fusion methods. Through the novel attention gating mechanism, higher recognition accuracy is achieved while significantly reducing the number of EEG channels, providing new ideas and approaches based on attention mechanisms and gated fusion for multimodal emotion recognition in resource-constrained environments. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (4th Edition))

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23 pages, 2817 KB

Open AccessArticle

Characterizing and Optimizing Spatial Selectivity of Peripheral Nerve Stimulation Montages and Electrode Configurations In Silico

by Jonathan Brand, Ryan Kochis, Vasav Shah and Wentai Liu

Algorithms 2025, 18(10), 635; https://doi.org/10.3390/a18100635 - 9 Oct 2025

Viewed by 1094

Abstract

Spatially selective nerve stimulation is an active area of research, with the capability to reduce side effects and increase the clinical efficacy of nerve stimulation technologies. Several research groups have demonstrated proof-of-concept devices capable of performing spatially selective stimulation with multi-contact cuff electrodes in vivo; however, optimizing the technique is difficult due to the large possibility space granted by a multi-electrode cuff. Our work attempts to elucidate the most valuable stimulation montages (current ratios between stimulating electrodes) provided by a multi-contact cuff. We characterized the performance of five different montage types when stimulating fibers in different “electrode configurations”, with configurations including up to three rings of electrode contacts, 13 different counts of electrodes per ring, and five electrode arc lengths per electrode count (for 195 unique configurations). Selected montages included several methods from prior art, as well as our own. Among montage types, the most spatially selective stimulation was one we refer to as “X-Adjacent” stimulation, in which three adjacent electrodes are active per ring. Optimized X-adjacent montages achieved an average fiber specificity of 71.9% for single-ring electrode configurations when stimulating fibers located at a depth of two-thirds of the nerve radius, and an average fiber specificity of 77.2% for two-ring configurations. These values were the highest among montages tested, and in combination with our other metrics, led these montages to perform best in the majority of cost functions investigated. This success leads us to recommend X-Adjacent montages to researchers exploring spatially selective stimulation. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (4th Edition))

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22 pages, 372 KB

Open AccessReview

A Structured Review of EEG-Based Machine Learning Approaches for Brain Age Prediction

by Ruslan Zhulduzbayev, Arian Ashourvan, Diana Arman, Alibek Bissembayev and Almira Kustubayeva

Algorithms 2026, 19(1), 91; https://doi.org/10.3390/a19010091 - 22 Jan 2026

Viewed by 903

Abstract

The determination of brain age based on electroencephalography (EEG) data has become widely developed with the spread of machine learning in recent years. In this research paper, we analyzed 21 articles published no earlier than 2015, focusing particularly on features, machine learning and deep learning models, and the validation process. The studies reviewed presented model performance on EEG data using machine learning or deep learning techniques. Deep convolutional and transformer-based models trained on well-curated features forecasted chronological age most precisely. In newborns, time–frequency and entropy-based characteristics showed good predictive power for the brain age index (BAI) and functional brain age (FBA). Consistently, spectral and nonlinear descriptors ranked among the most informative characteristics. Methodological rigor, meanwhile, differed: only a small number of studies used bias correction techniques, addressed statistical assumptions, or reported external validation. Preprocessing techniques also showed significant variation. Although EEG-based models have good accuracy, problems of interpretability and generalizability restrict their clinical and developmental use. Advancing this discipline will call for biologically based outcome definitions, uniform evaluation systems, and open source processing pipelines. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (4th Edition))

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27 pages, 957 KB

Open AccessEditor’s ChoiceReview

Deep Learning for Brain MRI Tissue and Structure Segmentation: A Comprehensive Review

by Nedim Šišić and Peter Rogelj

Algorithms 2025, 18(10), 636; https://doi.org/10.3390/a18100636 - 9 Oct 2025

Cited by 4 | Viewed by 4013

Abstract

Brain MRI segmentation plays a crucial role in neuroimaging studies and clinical trials by enabling the precise localization and quantification of brain tissues and structures. The advent of deep learning has transformed the field, offering accurate and fast tools for MRI segmentation. Nevertheless, several challenges limit the widespread applicability of these methods in practice. In this systematic review, we provide a comprehensive analysis of developments in deep learning-based segmentation of brain MRI in adults, segmenting the brain into tissues, structures, and regions of interest. We explore the key model factors influencing segmentation performance, including architectural design, choice of input size and model dimensionality, and generalization strategies. Furthermore, we address validation practices, which are particularly important given the scarcity of manual annotations, and identify the limitations of current methodologies. We present an extensive compilation of existing segmentation works and highlight the emerging trends and key results. Finally, we discuss the challenges and potential future directions in the field. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (4th Edition))

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