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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 2027 | Viewed by 10539

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 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. 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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Published Papers (7 papers)

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Research

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31 pages, 8223 KB  
Article
X-ViTCNN: A Novel Network-Level Fusion of Transfer Learning and Customized Vision Transformer for Multi-Stage Alzheimer’s Disease Prediction Using MRI Scans
by Armughan Ali, Hooria Shahbaz, Shahid Mohammad Ganie and Manahil Mohammed Alfuraydan
Diagnostics 2026, 16(6), 835; https://doi.org/10.3390/diagnostics16060835 - 11 Mar 2026
Cited by 1 | Viewed by 544
Abstract
Background/Objectives: Alzheimer’s disease (AD), the most prevalent form of dementia, is characterized by an overall decline in cognitive functioning and represents a major public health crisis. It remains critical to be able to accurately and quickly diagnose patients with AD; however, recent deep [...] Read more.
Background/Objectives: Alzheimer’s disease (AD), the most prevalent form of dementia, is characterized by an overall decline in cognitive functioning and represents a major public health crisis. It remains critical to be able to accurately and quickly diagnose patients with AD; however, recent deep learning approaches using MRI data do not provide sample generalization, have high computational requirements, and offer little interpretability. Methods: In this study, we present a new framework called eXplorative ViT-CNN (X-ViTCNN) that combines a customized Vision Transformer model with two previously trained CNNs (DenseNet201 and MobileNetV2). With our proposed preprocessing approach using contrast-enhanced preprocessing to highlight neuroanatomical features as well as Bayesian Optimization to tune hyperparameters, we fuse local structural features originating from the CNNs with global representations from the transformer and feed the final result to fully connected dense layers for multi-stage classification. We also use Grad-CAM visualizations to provide insight into how our model arrived at its classification. Results: Experiments conducted on ADNI and OASIS datasets demonstrate the superiority of X-ViTCNN, achieving accuracies of 97.98% and 94.52%, respectively. The model outperformed individual baselines and other pre-trained architectures, showing balanced sensitivity and specificity across all AD stages. Conclusions: The proposed X-ViTCNN framework is a powerful, interpretable method for predicting the development of multi-stage Alzheimer’s disease using MRI scans. The combination of complementary feature learning, automatic hyperparameter optimization and interpretability through visualization make it an excellent potential tool for clinicians to support their decision making in the early diagnosis and ongoing monitoring of persons with Alzheimer’s disease. 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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29 pages, 3472 KB  
Article
TensorCSBP: A Tensor Center-Symmetric Feature Extractor for EEG Odor Detection
by Irem Tasci, Ilknur Sercek, Yunus Talu, Prabal Datta Barua, Mehmet Baygin, Burak Tasci, Sengul Dogan and Turker Tuncer
Diagnostics 2026, 16(5), 789; https://doi.org/10.3390/diagnostics16050789 - 6 Mar 2026
Viewed by 423
Abstract
Objective: Accurate odor classification from EEG signals requires informative and interpretable features. Although Local Binary Pattern (LBP) and variants such as the center-symmetric binary pattern are widely used, they lack sufficient explainability and tensor-level implementations. Additionally, neuroscientific understanding of odor processing remains [...] Read more.
Objective: Accurate odor classification from EEG signals requires informative and interpretable features. Although Local Binary Pattern (LBP) and variants such as the center-symmetric binary pattern are widely used, they lack sufficient explainability and tensor-level implementations. Additionally, neuroscientific understanding of odor processing remains limited. Methods: We propose Tensor Center-Symmetric Binary Pattern (TensorCSBP), a novel tensor-based feature extractor designed for EEG odor analysis. TensorCSBP is integrated into an explainable feature engineering (XFE) pipeline with four steps: (1) TensorCSBP for feature generation, (2) CWNCA for feature selection, (3) tkNN classifier for decision making, and (4) DLob method for symbolic interpretability. Results: TensorCSBP XFE was evaluated on a newly collected 32-channel EEG dataset for odor detection. It achieved 96.68% accuracy under 10-fold cross-validation. Conclusions: The information entropy of the DLob symbol sequence was 3.5675, demonstrating the richness of the interpretability output. Significance: This study presents a high-accuracy, explainable, and computationally efficient model for EEG-based odor classification. TensorCSBP bridges low-level signal patterns with symbolic neuroscience insights, offering real-time potential for BCI and 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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17 pages, 565 KB  
Article
Bridging Perception and Reasoning: An Evidence-Based Agentic System for Diagnosis and Treatment Recommendations of Vascular Anomalies
by Yize Zhang, Yajing Qiu and Xiaoxi Lin
Diagnostics 2026, 16(4), 621; https://doi.org/10.3390/diagnostics16040621 - 20 Feb 2026
Viewed by 559
Abstract
Background: Vascular anomalies (VAs), including hemangiomas and vascular malformations, present a significant diagnostic challenge due to their high prevalence, complex classification (nearly 100 subtypes), and visual mimicry. Current Multimodal Large Language Models (MLLMs) struggle in this specialized domain, often failing to capture fine-grained [...] Read more.
Background: Vascular anomalies (VAs), including hemangiomas and vascular malformations, present a significant diagnostic challenge due to their high prevalence, complex classification (nearly 100 subtypes), and visual mimicry. Current Multimodal Large Language Models (MLLMs) struggle in this specialized domain, often failing to capture fine-grained visual features or lacking evidence-based reasoning. To address these limitations, we introduce HevaDx, an agentic diagnostic system that explicitly decouples visual perception from clinical reasoning. Methods: Leveraging a newly constructed large-scale dataset of VA patients, HevaDx employs a lightweight visual specialist for precise feature extraction and a reasoning specialist equipped with Retrieval-Augmented Generation (RAG) for therapeutic planning. This cooperative architecture mitigates the “reasoning gap” observed in end-to-end models by grounding decisions in up-to-date clinical guidelines. Results: Experimental results demonstrate that HevaDx achieves high performance with a top-3 diagnostic accuracy of 94.8% and a treatment recommendation accuracy of 83.3%. Conclusions: By bridging visual precision with transparent, verifiable logic, HevaDx offers a reliable framework for AI-assisted management of vascular anomalies. 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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36 pages, 2586 KB  
Article
GPTNeXt: Biomedical Image Classification Investigations
by Fahad A. Alotaibi, Mehmet Said Nur Yagmahan, Khalid A. Alobaid, Mousa Jari, Omer Faruk Goktas, Mehmet Baygin, Turker Tuncer and Sengul Dogan
Diagnostics 2026, 16(4), 581; https://doi.org/10.3390/diagnostics16040581 - 14 Feb 2026
Viewed by 575
Abstract
Background/Objectives: In the field of computer vision, prominent solutions often rely on transformers and convolutional neural networks (CNNs). Researchers frequently incorporate CNNs and transformers in developing image classification models. This study aims to introduce an innovative CNN model inspired by the Generative [...] Read more.
Background/Objectives: In the field of computer vision, prominent solutions often rely on transformers and convolutional neural networks (CNNs). Researchers frequently incorporate CNNs and transformers in developing image classification models. This study aims to introduce an innovative CNN model inspired by the Generative Pretrained Transformer (GPT) architecture and assess its image classification capabilities. Methods: This study utilized three distinct biomedical image datasets to evaluate the efficacy of the proposed GPTNeXt model. The datasets encompassed (i) Alzheimer’s disease (AD) magnetic resonance (MR) images, (ii) blood images, and (iii) lung cancer images. The choice of these datasets aimed to showcase the GPTNeXt model’s versatile classification performance. The GPTNeXt model and a deep feature engineering approach based on it were developed. In this deep feature engineering model, features were extracted from the global average pooling layer of GPTNeXt, and a novel deep feature extraction method was employed. This method extracted features from the entire image and generated nine fixed-size patches. To identify the most informative features, iterative neighborhood component analysis (INCA) was applied. The classification phase involved three shallow classifiers to produce classification results. Results: The GPTNeXt-based feature engineering model was applied to the three aforementioned biomedical image datasets, achieving classification accuracies exceeding 98% for all of them. Conclusions: This study demonstrates the high effectiveness of the proposed approach, as evidenced by the exceptional classification performance on the selected biomedical image datasets. Additionally, a lightweight CNN was introduced, showcasing outstanding classification performance. 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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30 pages, 4399 KB  
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
Cited by 2 | Viewed by 2069
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 KB  
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
Cited by 1 | Viewed by 1734
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 KB  
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 7 | Viewed by 3497
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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