Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
1.8
Journals
Algorithms
Special Issues
Machine Learning in Medical Signal and Image Processing (3rd Edition)
share announcement

Machine Learning in Medical Signal and Image Processing (3rd Edition)

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 3605

Special Issue Editor

Dr. Maryam Ravan

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research on the development and medical applications of machine learning algorithms to this Special Issue, entitled “Machine Learning in Medical Signal and Image Processing”. We are looking for 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 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • 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 policies can be found here.

Related Special Issues

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 5586 KiB  
Article
iCOR: End-to-End Electrocardiography Morphology Classification Combining Multi-Layer Filter and BiLSTM
by Siti Nurmaini, Wisnu Jatmiko, Satria Mandala, Bambang Tutuko, Erwin Erwin, Alexander Edo Tondas, Annisa Darmawahyuni, Firdaus Firdaus, Muhammad Naufal Rachmatullah, Ade Iriani Sapitri, Anggun Islami, Akhiar Wista Arum and Muhammad Ikhwan Perwira
Algorithms 2025, 18(4), 236; https://doi.org/10.3390/a18040236 - 18 Apr 2025
Viewed by 138
Abstract
Accurate delineation of ECG signals is critical for effective cardiovascular diagnosis and treatment. However, previous studies indicate that models developed for specific datasets and environments perform poorly when used with varying ECG signal morphology characteristics. This paper presents a novel approach to ECG [...] Read more.
Accurate delineation of ECG signals is critical for effective cardiovascular diagnosis and treatment. However, previous studies indicate that models developed for specific datasets and environments perform poorly when used with varying ECG signal morphology characteristics. This paper presents a novel approach to ECG signal delineation using a multi-layer filter (MLF) combined with a bidirectional long short-term memory (BiLSTM) model, namely iCOR. The proposed iCOR architecture enhances noise removal and feature extraction, resulting in improved classification of the P-QRS-T-wave morphology with a simpler model. Our method is evaluated on a combination of two standard ECG databases, the Lobachevsky University Electrocardiography Database (LUDB) and QT Database (QTDB). It can be observed that the classification performance for unseen sets of LUDB datasets yields above 90.4% and 98% accuracy, for record-based and beat-based approaches, respectively. Beat-based approaches outperformed the record-based approach in overall performance metric results. Similar results were shown in an unseen set of the QTDB, in which beat-based approaches performed with accuracy above 97%. These results highlight the robustness and efficacy of the iCOR model across diverse ECG signal datasets. The proposed approach offers a significant advancement in ECG signal analysis, paving the way for more reliable and precise cardiac health monitoring. Full article
(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (3rd Edition))
►▼ Show Figures

Figure 1

26 pages, 8000 KiB  
Article
Patient-Specific Hyperparameter Optimization of a Deep Learning-Based Tumor Autocontouring Algorithm on 2D Liver, Prostate, and Lung Cine MR Images: A Pilot Study
by Gawon Han, Keith Wachowicz, Nawaid Usmani, Don Yee, Jordan Wong, Arun Elangovan, Jihyun Yun and B. Gino Fallone
Algorithms 2025, 18(4), 233; https://doi.org/10.3390/a18040233 - 18 Apr 2025
Viewed by 113
Abstract
Linear accelerator–magnetic resonance (linac-MR) hybrid systems allow for real-time magnetic resonance imaging (MRI)-guided radiotherapy for more accurate dose delivery to the tumor and improved sparing of the adjacent healthy tissues. However, for real-time tumor detection, it is unfeasible for a human expert to [...] Read more.
Linear accelerator–magnetic resonance (linac-MR) hybrid systems allow for real-time magnetic resonance imaging (MRI)-guided radiotherapy for more accurate dose delivery to the tumor and improved sparing of the adjacent healthy tissues. However, for real-time tumor detection, it is unfeasible for a human expert to manually contour (gold standard) the tumor at the fast imaging rate of a linac-MR. This study aims to develop a neural network-based tumor autocontouring algorithm with patient-specific hyperparameter optimization (HPO) and to validate its contouring accuracy using in vivo MR images of cancer patients. Two-dimensional (2D) intrafractional MR images were acquired at 4 frames/s using 3 tesla (T) MRI from 11 liver, 24 prostate, and 12 lung cancer patients. A U-Net architecture was applied for tumor autocontouring and was further enhanced by implementing HPO using the Covariance Matrix Adaptation Evolution Strategy. Six hyperparameters were optimized for each patient, for which intrafractional images and experts’ manual contours were input into the algorithm to find the optimal set of hyperparameters. For evaluation, Dice’s coefficient (DC), centroid displacement (CD), and Hausdorff distance (HD) were computed between the manual contours and autocontours. The performance of the algorithm was benchmarked against two standardized autosegmentation methods: non-optimized U-Net and nnU-Net. For the proposed algorithm, the mean (standard deviation) DC, CD, and HD of the 47 patients were 0.92 (0.04), 1.35 (1.03), and 3.63 (2.17) mm, respectively. Compared to the two benchmarking autosegmentation methods, the proposed algorithm achieved the best overall performance in terms of contouring accuracy and speed. This work presents the first tumor autocontouring algorithm applicable to the intrafractional MR images of liver and prostate cancer patients for real-time tumor-tracked radiotherapy. The proposed algorithm performs patient-specific HPO, enabling accurate tumor delineation comparable to that of experts. Full article
(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (3rd Edition))
►▼ Show Figures

Figure 1

19 pages, 7498 KiB  
Article
An Efficient Explainability of Deep Models on Medical Images
by Salim Khiat, Sidi Ahmed Mahmoudi, Sédrick Stassin, Lillia Boukerroui, Besma Senaï and Saïd Mahmoudi
Algorithms 2025, 18(4), 210; https://doi.org/10.3390/a18040210 - 9 Apr 2025
Viewed by 275
Abstract
Nowadays, Artificial Intelligence (AI) has revolutionized many fields and the medical field is no exception. Thanks to technological advancements and the emergence of Deep Learning (DL) techniques AI has brought new possibilities and significant improvements to medical practice. Despite the excellent results of [...] Read more.
Nowadays, Artificial Intelligence (AI) has revolutionized many fields and the medical field is no exception. Thanks to technological advancements and the emergence of Deep Learning (DL) techniques AI has brought new possibilities and significant improvements to medical practice. Despite the excellent results of DL models in terms of accuracy and performance, they remain black boxes as they do not provide meaningful insights into their internal functioning. This is where the field of Explainable AI (XAI) comes in, aiming to provide insights into the underlying workings of these black box models. In this present paper the visual explainability of deep models on chest radiography images are addressed. This research uses two datasets, the first on COVID-19, viral pneumonia, normality (healthy patients) and the second on pulmonary opacities. Initially the pretrained CNN models (VGG16, VGG19, ResNet50, MobileNetV2, Mixnet and EfficientNetB7) are used to classify chest radiography images. Then, the visual explainability methods (GradCAM, LIME, Vanilla Gradient, Gradient Integrated Gradient and SmoothGrad) are performed to understand and explain the decisions made by these models. The obtained results show that MobileNetV2 and VGG16 are the best models for the first and second datasets, respectively. As for the explainability methods, the results were subjected to doctors and were validated by calculating the mean opinion score. The doctors deemed GradCAM, LIME and Vanilla Gradient as the most effective methods, providing understandable and accurate explanations. Full article
(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (3rd Edition))
►▼ Show Figures

Figure 1

17 pages, 16395 KiB  
Article
Towards Effective Parkinson’s Monitoring: Movement Disorder Detection and Symptom Identification Using Wearable Inertial Sensors
by Umar Khan, Qaiser Riaz, Mehdi Hussain, Muhammad Zeeshan and Björn Krüger
Algorithms 2025, 18(4), 203; https://doi.org/10.3390/a18040203 - 4 Apr 2025
Viewed by 316
Abstract
Parkinson’s disease lacks a cure, yet symptomatic relief can be achieved through various treatments. This study dives into the critical aspect of anomalous event detection in the activities of daily living of patients with Parkinson’s disease and the identification of associated movement disorders, [...] Read more.
Parkinson’s disease lacks a cure, yet symptomatic relief can be achieved through various treatments. This study dives into the critical aspect of anomalous event detection in the activities of daily living of patients with Parkinson’s disease and the identification of associated movement disorders, such as tremors, dyskinesia, and bradykinesia. Utilizing the inertial data acquired from the most affected upper limb of the patients, this study aims to create an optimal pipeline for Parkinson’s patient monitoring. This study proposes a two-stage movement disorder detection and classification pipeline for binary classification (normal or anomalous event) and multi-label classification (tremors, dyskinesia, and bradykinesia), respectively. The proposed pipeline employs and evaluates manual feature crafting for classical machine learning algorithms, as well as an RNN-CNN-inspired deep learning model that does not require manual feature crafting. This study also explore three different window sizes for signal segmentation and two different auto-segment labeling approaches for precise and correct labeling of the continuous signal. The performance of the proposed model is validated on a publicly available inertial dataset. Comparisons with existing works reveal the novelty of our approach, covering multiple anomalies (tremors, dyskinesia, and bradykinesia) and achieving 93.03% recall for movement disorder detection (binary) and 91.54% recall for movement disorder classification (multi-label). We believe that the proposed approach will advance the field towards more effective and comprehensive solutions for Parkinson’s detection and symptom classification. Full article
(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (3rd Edition))
►▼ Show Figures

Figure 1

16 pages, 702 KiB  
Article
A Structural Analysis of AI Implementation Challenges in Healthcare
by Q Angelina, Khadija Begum, Hee-Cheol Kim, Sushanta Tripathy, Deepak Singhal and Saranjit Singh
Algorithms 2025, 18(4), 189; https://doi.org/10.3390/a18040189 - 26 Mar 2025
Viewed by 1010
Abstract
The incorporation of artificial intelligence (AI) into the healthcare system has been revolutionized, promising key advancements in diagnosis, treatment, patient care, administrative tasks, and operational efficiency. Using an in-depth analysis of the extensive amount of research on artificial intelligence and how it could [...] Read more.
The incorporation of artificial intelligence (AI) into the healthcare system has been revolutionized, promising key advancements in diagnosis, treatment, patient care, administrative tasks, and operational efficiency. Using an in-depth analysis of the extensive amount of research on artificial intelligence and how it could help the medical industry, this study identified eleven barriers and challenges. Interpretive structural modeling (ISM) was used as a methodological approach to determine the relationship between the difficulties extracted and their dependency and driving powers. It resulted in a five-tiered model, with the introduction of innovative and new-generation tools topping the chart as the most dependent challenge. Similarly, Insufficient Data, Data Acquisition, Data Misuse, and Missing Compassion were the key drivers. Therefore, during the implementation of artificial intelligence in medicine, these challenges should be considered. Although artificial intelligence (AI) possesses the groundbreaking power to enhance patient care and operational efficiency in the healthcare sector, there are several key problems that must be addressed for implementation to be fruitful. The order of these challenges was ascertained through interpretive structural modeling, underlining the significance of innovation and data-related issues. Health systems can optimize AI’s benefits and enhance diagnosis, patient care, and overall hospital management by aggressively eliminating its deterrents. Full article
(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (3rd Edition))
►▼ Show Figures

Figure 1

17 pages, 1944 KiB  
Article
Pediatric Pneumonia Recognition Using an Improved DenseNet201 Model with Multi-Scale Convolutions and Mish Activation Function
by Petra Radočaj, Dorijan Radočaj and Goran Martinović
Algorithms 2025, 18(2), 98; https://doi.org/10.3390/a18020098 - 10 Feb 2025
Viewed by 797
Abstract
Pediatric pneumonia remains a significant global health issue, particularly in low- and middle-income countries, where it contributes substantially to mortality in children under five. This study introduces a deep learning model for pediatric pneumonia diagnosis from chest X-rays that surpasses the performance of [...] Read more.
Pediatric pneumonia remains a significant global health issue, particularly in low- and middle-income countries, where it contributes substantially to mortality in children under five. This study introduces a deep learning model for pediatric pneumonia diagnosis from chest X-rays that surpasses the performance of state-of-the-art methods reported in the recent literature. Using a DenseNet201 architecture with a Mish activation function and multi-scale convolutions, the model was trained on a dataset of 5856 chest X-ray images, achieving high performance: 0.9642 accuracy, 0.9580 precision, 0.9506 sensitivity, 0.9542 F1 score, and 0.9507 specificity. These results demonstrate a significant advancement in diagnostic precision and efficiency within this domain. By achieving the highest accuracy and F1 score compared to other recent work using the same dataset, our approach offers a tangible improvement for resource-constrained environments where access to specialists and sophisticated equipment is limited. While the need for high-quality datasets and adequate computational resources remains a general consideration for deep learning applications, our model’s demonstrably superior performance establishes a new benchmark and offers the delivery of more timely and precise diagnoses, with the potential to significantly enhance patient outcomes. Full article
(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (3rd Edition))
►▼ Show Figures

Figure 1

22 pages, 20326 KiB  
Article
GATransformer: A Graph Attention Network-Based Transformer Model to Generate Explainable Attentions for Brain Tumor Detection
by Sara Tehsin, Inzamam Mashood Nasir and Robertas Damaševičius
Algorithms 2025, 18(2), 89; https://doi.org/10.3390/a18020089 - 6 Feb 2025
Cited by 2 | Viewed by 911
Abstract
Brain tumors profoundly affect human health owing to their intricacy and the difficulties associated with early identification and treatment. Precise diagnosis is essential for effective intervention; nevertheless, the resemblance among tumor forms often complicates the identification of brain tumor types, particularly in the [...] Read more.
Brain tumors profoundly affect human health owing to their intricacy and the difficulties associated with early identification and treatment. Precise diagnosis is essential for effective intervention; nevertheless, the resemblance among tumor forms often complicates the identification of brain tumor types, particularly in the early stages. The latest deep learning systems offer very high classification accuracy but lack explainability to help patients understand the prediction process. GATransformer, a graph attention network (GAT)-based Transformer, uses the attention mechanism, GAT, and Transformer to identify and preserve key neural network channels. The channel attention module extracts deeper properties from weight-channel connections to improve model representation. Integrating these elements results in a reduction in model size and enhancement in computing efficiency, while preserving adequate model performance. The proposed model is assessed using two publicly accessible datasets, FigShare and Kaggle, and is cross-validated using the BraTS2019 and BraTS2020 datasets, demonstrating high accuracy and explainability. Notably, GATransformer generates interpretable attention maps, visually highlighting tumor regions to aid clinical understanding in medical imaging. Full article
(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (3rd Edition))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop