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Algorithms
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Machine Learning in Medical Signal and Image Processing (2nd Edition)
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Machine Learning in Medical Signal and Image Processing (2nd 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 September 2024 | Viewed by 6101

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 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

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

Related Special Issue

Published Papers (5 papers)

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Research

17 pages, 8135 KiB  
Article
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
Viewed by 575
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 [...] Read more.
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  
Article
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
Viewed by 1810
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 [...] Read more.
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  
Article
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 550
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 [...] Read more.
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  
Article
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 1434
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 [...] Read more.
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  
Article
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 1159
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 [...] Read more.
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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