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Mathematics
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Artificial Intelligence for Biomedical Image Processing and Data Analysis
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Artificial Intelligence for Biomedical Image Processing and Data Analysis

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 10 July 2025 | Viewed by 5285

Special Issue Editor

Dr. Yongmin Li

E-Mail Website
Guest Editor
Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK
Interests: artificial intelligence; data science; image processing; medical imaging; automatic control

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue of the journal Mathematics, entitled “Artificial Intelligence for Biomedical Image Processing and Data Analysis”. This Special Issue focuses on the application of mathematical principles and techniques in the intersection of artificial intelligence (AI), medical imaging, and data analysis. Artificial intelligence has revolutionized the field of biomedical image analysis by enabling automated and accurate interpretation of medical images. It encompasses various AI techniques such as machine learning, deep learning, and computer vision, which can extract meaningful information from complex medical images and aid in diagnosis, treatment planning, and disease monitoring. Moreover, data analysis plays a crucial role in the medical domain as it involves processing and interpreting large volumes of biomedical data to discover patterns, identify trends, and make informed decisions. We welcome contributions from researchers and practitioners in the field. The submission of papers addressing various aspects of AI for biomedical image processing and data analysis is encouraged. Topics of interest include image segmentation, image processing, feature extraction, object detection and computer vision, and machine-learning-based approaches for medical image analysis and data science.

Dr. Yongmin Li
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. Mathematics 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
  • machine learning
  • data science
  • image processing
  • image segmentation
  • medical imaging
  • computer vision
  • biomedical engineering
  • healthcare technologies

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Published Papers (3 papers)

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Research

32 pages, 10548 KiB  
Article
GAN-SkipNet: A Solution for Data Imbalance in Cardiac Arrhythmia Detection Using Electrocardiogram Signals from a Benchmark Dataset
by Hari Mohan Rai, Joon Yoo and Serhii Dashkevych
Mathematics 2024, 12(17), 2693; https://doi.org/10.3390/math12172693 - 29 Aug 2024
Cited by 9 | Viewed by 1730
Abstract
Electrocardiography (ECG) plays a pivotal role in monitoring cardiac health, yet the manual analysis of ECG signals is challenging due to the complex task of identifying and categorizing various waveforms and morphologies within the data. Additionally, ECG datasets often suffer from a significant [...] Read more.
Electrocardiography (ECG) plays a pivotal role in monitoring cardiac health, yet the manual analysis of ECG signals is challenging due to the complex task of identifying and categorizing various waveforms and morphologies within the data. Additionally, ECG datasets often suffer from a significant class imbalance issue, which can lead to inaccuracies in detecting minority class samples. To address these challenges and enhance the effectiveness and efficiency of cardiac arrhythmia detection from imbalanced ECG datasets, this study proposes a novel approach. This research leverages the MIT-BIH arrhythmia dataset, encompassing a total of 109,446 ECG beats distributed across five classes following the Association for the Advancement of Medical Instrumentation (AAMI) standard. Given the dataset’s inherent class imbalance, a 1D generative adversarial network (GAN) model is introduced, incorporating the Bi-LSTM model to synthetically generate the two minority signal classes, which represent a mere 0.73% fusion (F) and 2.54% supraventricular (S) of the data. The generated signals are rigorously evaluated for similarity to real ECG data using three key metrics: mean squared error (MSE), structural similarity index (SSIM), and Pearson correlation coefficient (r). In addition to addressing data imbalance, the work presents three deep learning models tailored for ECG classification: SkipCNN (a convolutional neural network with skip connections), SkipCNN+LSTM, and SkipCNN+LSTM+Attention mechanisms. To further enhance efficiency and accuracy, the test dataset is rigorously assessed using an ensemble model, which consistently outperforms the individual models. The performance evaluation employs standard metrics such as precision, recall, and F1-score, along with their average, macro average, and weighted average counterparts. Notably, the SkipCNN+LSTM model emerges as the most promising, achieving remarkable precision, recall, and F1-scores of 99.3%, which were further elevated to an impressive 99.60% through ensemble techniques. Consequently, with this innovative combination of data balancing techniques, the GAN-SkipNet model not only resolves the challenges posed by imbalanced data but also provides a robust and reliable solution for cardiac arrhythmia detection. This model stands poised for clinical applications, offering the potential to be deployed in hospitals for real-time cardiac arrhythmia detection, thereby benefiting patients and healthcare practitioners alike. Full article
(This article belongs to the Special Issue Artificial Intelligence for Biomedical Image Processing and Data Analysis)
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25 pages, 9867 KiB  
Article
Computer-Aided Diagnosis of Diabetic Retinopathy Lesions Based on Knowledge Distillation in Fundus Images
by Ernesto Moya-Albor, Alberto Lopez-Figueroa, Sebastian Jacome-Herrera, Diego Renza and Jorge Brieva
Mathematics 2024, 12(16), 2543; https://doi.org/10.3390/math12162543 - 17 Aug 2024
Cited by 2 | Viewed by 1270
Abstract
At present, the early diagnosis of diabetic retinopathy (DR), a possible complication of diabetes due to elevated glucose concentrations in the blood, is usually performed by specialists using a manual inspection of high-resolution fundus images based on lesion screening, leading to problems such [...] Read more.
At present, the early diagnosis of diabetic retinopathy (DR), a possible complication of diabetes due to elevated glucose concentrations in the blood, is usually performed by specialists using a manual inspection of high-resolution fundus images based on lesion screening, leading to problems such as high work-intensity and accessibility only in specialized health centers. To support the diagnosis of DR, we propose a deep learning-based (DL) DR lesion classification method through a knowledge distillation (KD) strategy. First, we use the pre-trained DL architecture, Inception-v3, as a teacher model to distill the dataset. Then, a student model, also using the Inception-v3 model, is trained on the distilled dataset to match the performance of the teacher model. In addition, a new combination of Kullback–Leibler (KL) divergence and categorical cross-entropy (CCE) loss is used to measure the difference between the teacher and student models. This combined metric encourages the student model to mimic the predictions of the teacher model. Finally, the trained student model is evaluated on a validation dataset to assess its performance and compare it with both the teacher model and another competitive DL model. Experiments are conducted on the two datasets, corresponding to an imbalanced and a balanced dataset. Two baseline models (Inception-v3 and YOLOv8) are evaluated for reference, obtaining a maximum training accuracy of 66.75% and 90.90%, respectively, and a maximum validation accuracy of 35.94% and 81.52%, both for the imbalanced dataset. On the other hand, the proposed DR classification model achieves an average training accuracy of 99.01% and an average validation accuracy of 97.30%, overcoming the baseline models and other state-of-the-art works. Experimental results show that the proposed model achieves competitive results in DR lesion detection and classification tasks, assisting in the early diagnosis of diabetic retinopathy. Full article
(This article belongs to the Special Issue Artificial Intelligence for Biomedical Image Processing and Data Analysis)
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19 pages, 2006 KiB  
Article
Shearlet Transform Applied to a Prostate Cancer Radiomics Analysis on MR Images
by Rosario Corso, Alessandro Stefano, Giuseppe Salvaggio and Albert Comelli
Mathematics 2024, 12(9), 1296; https://doi.org/10.3390/math12091296 - 25 Apr 2024
Cited by 9 | Viewed by 1480
Abstract
For decades, wavelet theory has attracted interest in several fields in dealing with signals. Nowadays, it is acknowledged that it is not very suitable to face aspects of multidimensional data like singularities and this has led to the development of other mathematical tools. [...] Read more.
For decades, wavelet theory has attracted interest in several fields in dealing with signals. Nowadays, it is acknowledged that it is not very suitable to face aspects of multidimensional data like singularities and this has led to the development of other mathematical tools. A recent application of wavelet theory is in radiomics, an emerging field aiming to improve diagnostic, prognostic and predictive analysis of various cancer types through the analysis of features extracted from medical images. In this paper, for a radiomics study of prostate cancer with magnetic resonance (MR) images, we apply a similar but more sophisticated tool, namely the shearlet transform which, in contrast to the wavelet transform, allows us to examine variations along more orientations. In particular, we conduct a parallel radiomics analysis based on the two different transformations and highlight a better performance (evaluated in terms of statistical measures) in the use of the shearlet transform (in absolute value). The results achieved suggest taking the shearlet transform into consideration for radiomics studies in other contexts. Full article
(This article belongs to the Special Issue Artificial Intelligence for Biomedical Image Processing and Data Analysis)
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