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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics
M. Pyingkodi
Department of Computer Applications, Kongu Engineering College, Perundurai, India
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Dynamic Uncertainty-Aware Adaptive Subspace Fusion Network for Robust Multimodal Medical Image Classification Krishnakumar B; Thanga Parvathi; K. Nithya; M. Pyingkodi; Kunchanapalli Rama Krishna; Jeevitha R
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1529

Abstract

Multimodal medical image classification leverages complementary information from multiple imaging modalities to improve diagnostic accuracy and clinical decision-making. However, most existing multimodal fusion approaches rely on deterministic low-rank constraints and assume equal importance across all modalities. Such assumptions significantly limit flexibility, robustness, and interpretability, particularly in real-world clinical scenarios where modality data may be noisy, incomplete, or partially missing. To address these challenges, this work proposes a Dynamic Uncertainty-Aware Adaptive Subspace Fusion Network (DUA-SFNet) for robust multimodal medical image classification. The core of the proposed framework is a rank-learning adaptive-rank tensor decomposition module that dynamically adjusts subspace dimensionality according to the intrinsic complexity of the input data. This adaptive mechanism effectively reduces feature redundancy while preserving the highly discriminative information essential for accurate classification. In addition, DUA-SFNet incorporates a modality uncertainty estimation scheme to explicitly quantify the reliability and trustworthiness of each modality. By assigning uncertainty-aware weights during the fusion process, the framework can suppress unreliable or noisy modalities while emphasizing more informative ones, thereby improving resilience under adverse data conditions. Furthermore, a hierarchical adaptive attention strategy is employed to jointly model intra-subspace feature interactions and inter-modality dependencies. This design enhances feature representation capability while offering improved clinical interpretability by revealing how different modalities and subspaces contribute to the final decision. Extensive experiments conducted on multiple public and self-organized multimodal medical image datasets demonstrate that DUA-SFNet consistently outperforms state-of-the-art methods, achieving classification accuracy improvements of 3.8–6.2% and F1-score gains of 4.1–7.5%. Overall, DUA-SFNet provides an interpretable, uncertainty-aware, and adaptive solution for next-generation multimodal medical image analysis.
Optimized Multi-Resolution Attention-Based Architecture for Effective Diabetic Skin Lesion Classification B. K. Jaleesha; Suganthi Suganthi; N. K. Priyadharsini; Yuvaraj S.; M. Pyingkodi; M. Vallikkannu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1320

Abstract

Early and reliable identification of diabetic skin complications, including ischemia and infection, is essential for timely clinical intervention and prevention of severe outcomes. Nevertheless, traditional deep learning models often exhibit limited generalization capability and high computational demands, particularly when distinguishing between visually subtle infection types. To overcome these challenges, this study introduces an end-to-end deep learning architecture termed the Enhanced Multi-Resolution Multi-Path Attention Network (EMRMP-Net), specifically designed for robust diabetic lesion classification. A key contribution of this work is the introduction of a trainable attention-based fusion mechanism that adaptively learns to weight and integrate multi-resolution feature maps, enhancing contextual understanding and discriminative performance. To address the prevalent issue of class imbalance in medical imaging datasets, EMRMP-Net utilizes focal loss and domain-tailored data augmentation, thereby promoting stable learning and improved representation of minority classes. Additionally, a shared classification head across multiple resolution pathways enables joint feature optimization, reducing computational redundancy and improving learning efficiency compared to traditional MRMP models. Comprehensive experiments on the publicly available Diabetic Foot Ulcer (DFU) dataset demonstrate that EMRMP-Net surpasses existing state-of-the-art-methods, achieving 98.12% accuracy and 98.14% F1-score for ischemia detection, and 95.27% accuracy with 93.68% F1-score for infection classification. Overall, EMRMP-Net provides a highly effective, computationally efficient, and generalizable framework for automated diabetic skin lesion analysis, demonstrating strong potential for real-world clinical applications. EMRMP-Net is designed as a general framework for diabetic skin lesion analysis, capable of handling diverse lesion characteristics through multi-resolution and attention-based feature learning. However, in this work, the model is explicitly formulated, trained, and evaluated for the clinically critical binary classification task of distinguishing ischemic ulcers from infected ulcers within DFU imagery.
Co-Authors B. K. Jaleesha Jeevitha R K. Nithya Krishnakumar B Kunchanapalli Rama Krishna M. Vallikkannu N. K. Priyadharsini Suganthi Suganthi Thanga Parvathi Yuvaraj S.