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Recent Advances in Machine Learning and Explainable Artificial Intelligence in Biomedical Data Mining, and Disease Diagnosis Frameworks—2nd Edition

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A Special Issue of Bioengineering (ISSN 2306-5354) belonging to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 9157

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Prof. Dr. Amad Zafar

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Department of Artificial Intelligent and Robotics, Sejong University, Seoul 05006, Republic of Korea
Interests: biomedical signal/image processing; computer-aided diagnostic; brain imaging; brain–computer interface; machine learning; artificial intelligence; EEG; fNIRS
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Special Issue Information

Dear Colleagues,

Following the successful launch of the first Special Issue, titled ‘Recent Advances in Machine Learning and Explainable Artificial Intelligence in Biomedical Data Mining and Disease Diagnosis Frameworks,’ this Second Issue continues to highlight cutting-edge research at the intersection of biomedical data science, machine learning, and explainable AI.

The rapid evolution of artificial intelligence, data analytics, and technology has created new avenues for personalized healthcare approaches. This Special Issue focuses on the most recent advances in machine learning (ML) and explainable artificial intelligence (XAI) for biomedical data mining and disease diagnostic frameworks. This Special Issue delves into the application of advanced ML methods like deep learning and ensemble learning for analyzing intricate biomedical data sets, particularly focusing on disease diagnosis and prognosis. Another central theme of the Special Issue is the importance of explainable AI in healthcare applications. XAI techniques aim to make the decision-making process of AI systems more transparent and understandable. The potential topics include, but are not limited to, the following: supervised and unsupervised learning, deep learning, XAI in healthcare, system modelling and system design, confidentiality and privacy of health data, biometrics, digital technologies, data mining, computer-aided diagnosis, brain–computer interfaces, etc. This Special Issue aims to bring together original research and review papers on current breakthroughs in MI and XAI in healthcare.

Prof. Dr. Amad Zafar
Guest Editor

Dr. Sara Tehsin
Guest Editor Assistant
Email: sara.tehsin@ktu.edu
Centre of Real Time Computer Systems, Kaunas University of Technology, 51368 Kaunas, Lithuania
Interests: machine learning; image forensics; AI explainability; autonomous systems

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Keywords

EEG
fNIRS
MRI
X-rays
biomedical signal and image processing
machine learning
explainable artificial intelligence
biomedical data mining
computer-aided diagnosis
brain–computer interfaces
healthcare

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Recent Advances in Machine Learning and Explainable Artificial Intelligence in Biomedical Data Mining, and Disease Diagnosis Frameworks in Bioengineering (14 articles)

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Research

14 pages, 5378 KB

Open AccessArticle

Automated Craniofacial Artery Segmentation with Vessel Enhancement-Guided Deep Learning

by Hyeonju Park, Young Chul Kim, Kyoyeong Koo, Sangyun Kang, Jong Woo Choi and Chan-Ung Park

Bioengineering 2026, 13(7), 728; https://doi.org/10.3390/bioengineering13070728 - 24 Jun 2026

Viewed by 493

Abstract

Computed tomography angiography (CTA)-based segmentation of the superficial temporal arteries (STAs) and facial vessels (FVs) is important for neurosurgical and reconstructive planning. Nevertheless, segmentation of STAs and FVs remains challenging because of their small caliber, tortuous courses, and proximity to high-intensity bony structures. This study aims to develop a deep learning framework for accurate automated segmentation of these craniofacial vessels. A single-input 3D nnU-Net v2 model was trained using raw CTA volumes, while a Fusion-based Vesselness Map (FVM) was constructed from multiscale vessel-enhancement filters to emphasize small vascular structures and suppress irrelevant regions such as the skull and skin. Instead of being used as an additional input channel, the FVM was incorporated into the loss function as a spatial prior to guide the network toward vessel boundaries and distal branches. In 72 clinical cases, the FVM-guided model improved segmentation accuracy compared with a baseline model trained with Dice Focal Loss, particularly in boundary delineation. For the STAs, the Average Symmetric Surface Distance decreased from 6.543 mm to 2.941 mm. Qualitative evaluation further showed reduced segmentation noise and fewer false positives near bone and distal branches. These findings suggest that integrating classical vessel enhancement into deep learning supervision can improve morphologically consistent craniofacial vessel segmentation and support preoperative surgical planning. Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Explainable Artificial Intelligence in Biomedical Data Mining, and Disease Diagnosis Frameworks—2nd Edition)

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32 pages, 2767 KB

Open AccessArticle

Explainable Breast Cancer Detection Using Hierarchical Multi-Scale and Edge-Aware Transformer Networks

by Maria Altaib Badawi, Ehtisham Arshad, Armughan Ali, Oumaima Saidani, Taoufik Saidani, Zepa Yang and Yunyoung Nam

Bioengineering 2026, 13(6), 657; https://doi.org/10.3390/bioengineering13060657 - 3 Jun 2026

Viewed by 1312

Abstract

Breast cancer remains the leading cause of cancer-related deaths among women globally. Early detection through mammography is vital for improving survival rates; however, the large volume of medical images and subtle variations in lesion characteristics pose significant challenges to manual interpretation. Recent automated diagnostic models based on deep learning have shown strong potential for breast cancer classification, but challenges such as overfitting, high computational complexity, limited generalization, and insufficient interpretability remain unresolved. This paper proposes a computationally efficient and context-aware deep learning framework for breast cancer classification using transformer-based multi-scale attention mechanisms and explainable artificial intelligence (XAI). The proposed architecture integrates the Hierarchical Multi-Scale Transformer (HMT) and Edge-Aware Local Transformer (ELT) modules to jointly capture global contextual dependencies and boundary-sensitive local representations from mammographic images. ELT improves feature refinement in high-entropy regions, while HMT models global semantic interactions across multiple feature scales. In addition, an Adaptive Contextual Refinement (ACR) module is introduced to preserve semantically consistent feature representations across spatial resolutions. A Meta-Ensemble Classification (MEC) framework combining weighted SVM and K-Nearest Neighbors (KNN) classifiers is further employed using validation-guided class-adaptive weighting. The proposed framework is evaluated on four benchmark mammography datasets, namely CBIS-DDSM, DDSM, INBreast, and MIAS. The proposed model has demonstrated superior accuracy of over 99% across all breast cancer datasets. The model surpassed transformer-based baselines including Swin-T and ViT while maintaining lower parameter complexity and achieving approximately 7% higher accuracy on the CBIS-DDSM dataset. The proposed framework also demonstrated strong cross-dataset generalization and consistently achieved high precision, recall, and F1-score values across all benchmark datasets. To improve model interpretability, Grad-CAM, SHAP, Occlusion Sensitivity Analysis (OSA), and the proposed TIxAI consistency analysis framework are incorporated to provide preliminary explainability assessment for mammographic classification. The explainability analysis demonstrated spatially consistent saliency behavior across benchmark datasets; however, the current evaluation is based on internal saliency consistency rather than external clinical validation using expert lesion annotations. Overall, the proposed framework provides an effective and computationally efficient approach for automated breast cancer classification while improving model explainability and interpretability. Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Explainable Artificial Intelligence in Biomedical Data Mining, and Disease Diagnosis Frameworks—2nd Edition)

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43 pages, 3714 KB

Open AccessArticle

Cross-Center Vision–Language Transformer for Robust Mammography-Based Breast Cancer Diagnosis

by Anas W. Abulfaraj

Bioengineering 2026, 13(6), 653; https://doi.org/10.3390/bioengineering13060653 - 31 May 2026

Cited by 2 | Viewed by 896

Abstract

While promising results have been demonstrated for deep learning-based breast cancer diagnosis using mammography, problems persist in approaches that rely primarily on visual information. These problems include inadequate performance across diverse clinical centers, various imaging protocols, scanner types, and patient distributions. Here, we introduce Cross-Center Vision–Language Transformer (CC-VLT), a framework that integrates mammograms and clinical text to enable more robust, guided diagnosis. The framework incorporates a vision transformer for mammograms, a text transformer for salient clinical descriptors, bi-directional cross-modal attention for semantics, and a cross-center feature regularization approach to address the challenge of inter-institutional domain shifts. The framework is tested on a leave-one-center-out basis across several public mammography datasets and significantly outperforms strong baseline models in both intra- and cross-center evaluations. Our framework achieved an accuracy of 90.7% with an intra-center ROC–AUC of 0.951 and cross-center ROC–AUC results of 0.912, 0.927, and 0.934 on the CBIS-DDSM, INbreast, and VinDr-Mammo datasets, respectively. Reliability of the malignancy probability predictions improved, as evidenced by a diminished Expected Calibration Error and Brier Score. Our framework, by designing an effective integrated vision–language interaction model and implementing a cross-center feature regularization approach, sets a benchmark for robust breast cancer diagnosis across diverse clinical environments. Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Explainable Artificial Intelligence in Biomedical Data Mining, and Disease Diagnosis Frameworks—2nd Edition)

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21 pages, 2714 KB

Open AccessArticle

Sequential Transfer Learning for Multi-Domain Breast Image Segmentation Using a Transformer-Enhanced Hybrid U-Net

by Shagufta Manzoor, Javaria Amin and Amad Zafar

Bioengineering 2026, 13(5), 570; https://doi.org/10.3390/bioengineering13050570 - 18 May 2026

Cited by 1 | Viewed by 932

Abstract

Worldwide, breast cancer is the leading cause of death in women. This emphasizes the significance of an accurate breast cancer detection system. This study presents a unified framework for segmentation of breast cancer using multimodal imaging, such as histopathology, MRI, mammogram, and ultrasound. This framework integrates the CNN with Transformer modules and has three core technical innovations. First, features are extracted using an encoder–decoder design. The encoder has Residual Blocks with a base channel of 32, following feature extraction, which are progressively mapped and downsampled into four stages (32 → 64 → 128 → 256) of channels. The spatial channel is reduced using MaxPool2d operations from 256 × 256 to 128 × 128, 64 × 64, 32 × 32, and 16 × 16. After further convolutional refinement, a Transformer encoder is used on the 16 × 16 feature maps in the bottleneck. The Transformer comprises four encoders with multi-head self-attention (eight heads) and a 4.0 MLP ratio, enabling the model to capture local and global contextual dependencies at the lowest resolution. The proposed framework is trained with a learning rate of 1 × 10⁻⁴, up to 50 epochs with early stopping (patience = 12), using a combined Dice and binary cross-entropy loss that balances pixel-wise accuracy and overlap-based learning. Gradient clipping with a maximum norm of 5.0 is used to ensure training stability; ReduceLROnPlateau (factor = 0.5, patience = 5) is used to dynamically adjust the learning rate; and early stopping is used to prevent overfitting. To improve generalization and enhance robustness to data variability, data augmentation techniques such as random horizontal and vertical flips, intensity variations, and small rotations (±15°) are applied. Incremental learning was implemented in this study as a warm-start fine-tuning strategy, where the model was initialized based on learned weights from a previously trained model instead of training from scratch. This is done by loading saved checkpoints of the best-performing model and continuing training on a new dataset. The performance of the proposed framework is evaluated on four publicly available datasets and one local dataset, such as BUS-UCLM, BUSI, BreastDM, TNBC NucleiSegmentation, and BCSD-2024. The impressive results are achieved with Dice scores of 0.974 on ULCM, 0.975 on BUSI, 0.971 on BreastDM, 0.904 on TNBC nuclei segmentation, and 0.982 on BCSD-2024. The proposed model consistently performed better than classical U-Net models. Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Explainable Artificial Intelligence in Biomedical Data Mining, and Disease Diagnosis Frameworks—2nd Edition)

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27 pages, 32889 KB

Open AccessArticle

XAI-MedNet: A Next-Generation Explainable AI Framework for Contrast-Enhanced Skin Lesion Classification via Entropy-Controlled Optimization

by Abdulrahman Alabduljabbar, Tallha Akram, Youssef N. Altherwy, Muhammad Adeel Akram and Imran Ashraf

Bioengineering 2026, 13(5), 506; https://doi.org/10.3390/bioengineering13050506 - 27 Apr 2026

Viewed by 966

Abstract

Explainable Artificial Intelligence (XAI) has become a critical requirement in medical image analysis, where transparency and interpretability are essential for clinical trust and decision support. Melanoma is recognized as one of the most deadly types of skin cancer, with its occurrence exhibiting an increasing pattern in recent times. However, detecting this cancer in its initial stages greatly increases patients’ chances of long-term survival. Various computer-based techniques have recently been proposed to diagnose skin lesions at their early stages. Even though the machine learning community has achieved a certain degree of success, there is still an unresolved research challenge regarding high error margins and the limited interpretability of automated systems. This study focuses on addressing both segmentation and classification tasks, with particular emphasis on two key concepts: (1) improving image quality to maximize distinguishability between foreground and background regions, thereby enhancing visual interpretability and segmentation accuracy and (2) eliminating redundant and cluttered feature information to generate the most discriminative and compact feature representations. The input images are initially processed using a novel metaheuristic contrast-stretching method to estimate image-specific key parameters, thereby enhancing lesion boundary clarity in a clinically interpretable manner. Following this, the improved images are fed into selected pre-trained deep models, including DenseNet-201, Inception-ResNet v2, and NASNet-Mobile. The extracted features from all pre-trained models are fused to produce resultant vectors, which are then refined using a bio-inspired feature selection method, termed entropy-controlled whale optimization, to retain only the most informative attributes. The selected discriminative feature set is subsequently classified using multiple classifiers. The results indicate that the proposed framework achieves superior performance compared to existing methods in terms of accuracy, sensitivity, specificity, and F1-score. Additionally, it facilitates a more explainable, transparent, and structured diagnostic pipeline appropriate for medical applications. Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Explainable Artificial Intelligence in Biomedical Data Mining, and Disease Diagnosis Frameworks—2nd Edition)

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16 pages, 1470 KB

Open AccessArticle

Physics-Guided Deep Learning for Interpretable Biomedical Image Reconstruction and Pattern Recognition in Diagnostic Frameworks

by Akeel Qadir, Saad Arif, Prajoona Valsalan and Osama Khan

Bioengineering 2026, 13(4), 457; https://doi.org/10.3390/bioengineering13040457 - 13 Apr 2026

Cited by 1 | Viewed by 1242

Abstract

This study introduces a physics-guided deep learning architecture designed for the simulation, reconstruction, and pattern recognition of biomedical images. By explicitly integrating physical priors into the learning model, the framework addresses the black-box nature of traditional artificial intelligence (AI). It provides an explainable AI pathway that enhances diagnostic accuracy, robustness, and clinical interpretation. The proposed framework was evaluated through systematic simulation studies. It involved complex geometric configurations, multimodal physical fields, and noise-corrupted synthetic three-dimensional brain volumes. Quantitative analysis demonstrates consistent improvements in reconstruction fidelity, with the peak signal-to-noise ratio (PSNR) reaching 47 dB and the structural similarity index exceeding 0.90 across all scenarios. Notably, at moderate noise levels (0.05), the framework maintains a PSNR greater than 32 dB, ensuring structural integrity essential for computer-aided diagnosis. Volumetric brain experiments further reveal a 38–44% reduction in activation localization errors, highlighting the framework’s utility in functional imaging and disease prognosis. By grounding deep learning in physical constraints, this study provides a transparent and robust solution for automated disease classification and advanced biomedical imaging tasks within clinical decision support systems. Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Explainable Artificial Intelligence in Biomedical Data Mining, and Disease Diagnosis Frameworks—2nd Edition)

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31 pages, 3515 KB

Open AccessArticle

Improving Deep Learning Based Lung Nodule Classification Through Optimized Adaptive Intensity Correction

by Saba Khan, Muhammad Nouman Noor, Haya Mesfer Alshahrani, Wided Bouchelligua and Imran Ashraf

Bioengineering 2026, 13(4), 396; https://doi.org/10.3390/bioengineering13040396 - 29 Mar 2026

Cited by 1 | Viewed by 1076

Abstract

Lung cancer is one of the most common causes of death from cancer around the world, and catching it early through computed tomography (CT) scans can drastically improve survival. However, automated classification of pulmonary nodule candidates is hard because images do not all have the same intensity across scanners and protocols, resulting in inconsistent performance, more false positives (FP), and a ceiling on how much deep learning models work in an average clinic. In this work, we tackle this by introducing a preprocessing step that corrects intensity differences before feeding images into classification models. We use Contrast-Limited Adaptive Histogram Equalization (CLAHE), but with its key parameters tuned automatically via a modified version of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). This helps to boost local contrast adaptively, keeps important anatomical details intact, and cuts down on noise. We tested the approach on the public LUNA16 dataset, first checking image quality (Peak Signal-to-Noise Ratio (PSNR) around 53 dB and Structural Similarity Index (SSIM) of 0.9, better than standard methods), then training three popular deep models—namely, ResNet-50, EfficientNet-B0, and InceptionV3—with CutMix augmentation for better generalization. On the enhanced images, ResNet-50 achieved up to 99.0% classification accuracy with substantially less FP than when using the raw scans. Taken together, these results demonstrate that intelligent and optimized preprocessing can effectively mitigate intensity variations via deep learning for lung nodule detection, thus coming closer to realizing the practical toolbox of computer-aided diagnosis in routine clinical practice. Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Explainable Artificial Intelligence in Biomedical Data Mining, and Disease Diagnosis Frameworks—2nd Edition)

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39 pages, 17119 KB

Open AccessArticle

Transformer-Based Deep Learning for Population-Scale Retinal Image Screening of Ophthalmic Disorders

by Wiem Abdelbaki, Wided Bouchelligua, Inzamam Mashood Nasir, Sara Tehsin and Hend Alshaya

Bioengineering 2026, 13(4), 377; https://doi.org/10.3390/bioengineering13040377 - 25 Mar 2026

Viewed by 1129

Abstract

To perform screening of the retina on a population scale, an automated procedure is required that incorporates accurate, reproducible, interpretable, and computationally costeffective models. Existing approaches using convolutional or transformer architectures typically do not adequately represent both fine-grained pathology and large-scale retinal context simultaneously, which could adversely affect their reliability if used for large-scale applications in clinical practice. In this paper, we propose a hierarchical transformer-based screening framework for retinal fundus images that incorporates patch-based tokenization, global transformer encoding, and hierarchical aggregation of contextual information. We also developed a lightweight prediction head that supports screening for both single and multiple diseases. The framework has been evaluated using standard screening metrics, robustness, and cross-dataset generalization analyses on two eye retinopathy image databases: EyePACS and RFMiD. With regard to screening for a binary outcome of diabetic retinopathy, our method provided an accuracy of 89.4% and an area under the receiver operating characteristic (AUROC) curve of 93.6% on EyePACS and attained an accuracy of 95.2% and a macro-averaged F1 score of 82.7% on RFMiD. Our hierarchical transformer achieved improved robustness to degraded images and increased generalizability across datasets compared with all current state-of-the-art models. The proposed hierarchical transformer demonstrates strong potential for large-scale retinal screening and provides a promising foundation for future clinically validated deployment. Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Explainable Artificial Intelligence in Biomedical Data Mining, and Disease Diagnosis Frameworks—2nd Edition)

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