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Diagnostics
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Deep Learning in Biomedical Image and Signal Processing: Recent Advancements...
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Deep Learning in Biomedical Image and Signal Processing: Recent Advancements and Applications

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 2502

Special Issue Editor

Dr. Narinder Singh Punn

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, ABV-Indian Institute of Information Technology and Management Gwalior, Gwalior, Madhya Pradesh, India
Interests: deep learning; signal processing

Special Issue Information

Dear Colleagues,

This Special Issue aims to highlight the latest advancements in and innovative applications to deep learning in biomedical image and signal processing. We welcome original research articles, reviews, and perspectives that explore the development and application of deep learning algorithms for various tasks, including the following:

Image analysis: The segmentation, registration, classification, detection, and quantification of anatomical structures, lesions, and biomarkers from medical images (e.g., CT, MRI, PET, ultrasound, and microscopy).

Signal processing: The analysis and interpretation of physiological signals (e.g., ECG, EEG, EMG, and PPG) for disease diagnosis, monitoring, and prediction.

Multimodal data fusion: The integration of information from multiple imaging modalities and/or physiological signals for improved diagnostic accuracy and personalized medicine.

Explainable AI: The development of interpretable deep learning models to enhance trust and transparency in clinical decision making.

Clinical translation: The evaluation of the clinical impact and utility of deep learning algorithms in real-world settings.

Biomarker discovery and validation: Machine learning and deep learning approaches for identifying and validating novel biomarkers for early disease detection, prognosis, and treatment response prediction.

Predictive modeling and risk assessment: AI-based models for predicting disease progression, treatment outcomes, and identifying high-risk individuals for targeted interventions.

This Special Issue will provide a valuable resource for researchers, clinicians, and industry professionals interested in the latest developments in deep learning for biomedical applications.

Dr. Narinder Singh Punn
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. Diagnostics 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

  • deep learning
  • biomedical image
  • signal processing
  • multimodal data fusion
  • explainable AI

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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30 pages, 4399 KiB  
Article
Confident Learning-Based Label Correction for Retinal Image Segmentation
by Tanatorn Pethmunee, Supaporn Kansomkeat, Patama Bhurayanontachai and Sathit Intajag
Diagnostics 2025, 15(14), 1735; https://doi.org/10.3390/diagnostics15141735 - 8 Jul 2025
Viewed by 117
Abstract
Background/Objectives: In automatic medical image analysis, particularly for diabetic retinopathy, the accuracy of labeled data is crucial, as label noise can significantly complicate the analysis and lead to diagnostic errors. To tackle the issue of label noise in retinal image segmentation, an innovative [...] Read more.
Background/Objectives: In automatic medical image analysis, particularly for diabetic retinopathy, the accuracy of labeled data is crucial, as label noise can significantly complicate the analysis and lead to diagnostic errors. To tackle the issue of label noise in retinal image segmentation, an innovative label correction framework is introduced that combines Confident Learning (CL) with a human-in-the-loop re-annotation process to meticulously detect and rectify pixel-level labeling inaccuracies. Methods: Two CL-oriented strategies are assessed: Confident Joint Analysis (CJA) employing DeeplabV3+ with a ResNet-50 architecture, and Prune by Noise Rate (PBNR) utilizing ResNet-18. These methodologies are implemented on four publicly available retinal image datasets: HRF, STARE, DRIVE, and CHASE_DB1. After the models have been trained on the original labeled datasets, label noise is quantified, and amendments are executed on suspected misclassified pixels prior to the assessment of model performance. Results: The reduction in label noise yielded consistent advancements in accuracy, Intersection over Union (IoU), and weighted IoU across all the datasets. The segmentation of tiny structures, such as the fovea, demonstrated a significant enhancement following refinement. The Mean Boundary F1 Score (MeanBFScore) remained invariant, signifying the maintenance of boundary integrity. CJA and PBNR demonstrated strengths under different conditions, producing variations in performance that were dependent on the noise level and dataset characteristics. CL-based label correction techniques, when amalgamated with human refinement, could significantly enhance the segmentation accuracy and evaluation robustness for Accuracy, IoU, and MeanBFScore, achieving values of 0.9156, 0.8037, and 0.9856, respectively, with regard to the original ground truth, reflecting increases of 4.05%, 9.95%, and 1.28% respectively. Conclusions: This methodology represents a feasible and scalable solution to the challenge of label noise in medical image analysis, holding particular significance for real-world clinical applications. Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Image and Signal Processing: Recent Advancements and Applications)
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18 pages, 2705 KiB  
Article
Fusion-Based Deep Learning Approach for Renal Cell Carcinoma Subtype Detection Using Multi-Phasic MRI Data
by Gulhan Kilicarslan, Dilber Cetintas, Taner Tuncer and Muhammed Yildirim
Diagnostics 2025, 15(13), 1636; https://doi.org/10.3390/diagnostics15131636 - 26 Jun 2025
Viewed by 279
Abstract
Background/Objectives: Renal cell carcinoma (RCC) is a malignant disease that requires rapid and reliable diagnosis to determine the correct treatment protocol and to manage the disease effectively. However, the fact that the textural and morphological features obtained from medical images do not [...] Read more.
Background/Objectives: Renal cell carcinoma (RCC) is a malignant disease that requires rapid and reliable diagnosis to determine the correct treatment protocol and to manage the disease effectively. However, the fact that the textural and morphological features obtained from medical images do not differ even among different tumor types poses a significant diagnostic challenge for radiologists. In addition, the subjective nature of visual assessments made by experts and interobserver variability may cause uncertainties in the diagnostic process. Methods: In this study, a deep learning-based hybrid model using multiphase magnetic resonance imaging (MRI) data is proposed to provide accurate classification of RCC subtypes and to provide a decision support mechanism to radiologists. The proposed model performs a more comprehensive analysis by combining the T2 phase obtained before the administration of contrast material with the arterial (A) and venous (V) phases recorded after the injection of contrast material. Results: The model performs RCC subtype classification at the end of a five-step process. These are regions of interest (ROI), preprocessing, augmentation, feature extraction, and classification. A total of 1275 MRI images from different phases were classified with SVM, and 90% accuracy was achieved. Conclusions: The findings reveal that the integration of multiphase MRI data and deep learning-based models can provide a significant improvement in RCC subtype classification and contribute to clinical decision support processes. Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Image and Signal Processing: Recent Advancements and Applications)
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Review

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24 pages, 1667 KiB  
Review
Decoding Pain: A Comprehensive Review of Computational Intelligence Methods in Electroencephalography-Based Brain–Computer Interfaces
by Hadeel Alshehri, Abeer Al-Nafjan and Mashael Aldayel
Diagnostics 2025, 15(3), 300; https://doi.org/10.3390/diagnostics15030300 - 27 Jan 2025
Cited by 1 | Viewed by 1542
Abstract
Objective pain evaluation is crucial for determining appropriate treatment strategies in clinical settings. Studies have demonstrated the potential of using brain–computer interface (BCI) technology for pain classification and detection. Collating knowledge and insights from prior studies, this review explores the extensive work on [...] Read more.
Objective pain evaluation is crucial for determining appropriate treatment strategies in clinical settings. Studies have demonstrated the potential of using brain–computer interface (BCI) technology for pain classification and detection. Collating knowledge and insights from prior studies, this review explores the extensive work on pain detection based on electroencephalography (EEG) signals. It presents the findings, methodologies, and advancements reported in 20 peer-reviewed articles that utilize machine learning and deep learning (DL) approaches for EEG-based pain detection. We analyze various ML and DL techniques, support vector machines, random forests, k-nearest neighbors, and convolution neural network recurrent neural networks and transformers, and their effectiveness in decoding pain neural signals. The motivation for combining AI with BCI technology lies in the potential for significant advancements in the real-time responsiveness and adaptability of these systems. We reveal that DL techniques effectively analyze EEG signals and recognize pain-related patterns. Moreover, we discuss advancements and challenges associated with EEG-based pain detection, focusing on BCI applications in clinical settings and functional requirements for effective pain classification systems. By evaluating the current research landscape, we identify gaps and opportunities for future research to provide valuable insights for researchers and practitioners. Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Image and Signal Processing: Recent Advancements and Applications)
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