https://mail.jeeemi.org/index.php/jeeemi <p>The Journal of Electronics, Electromedical Engineering, and Medical Informatics, (JEEEMI), is a peer-reviewed periodical scientific journal aimed at publishing research results of the Journal focus areas. The Journal is published by the Department of Electromedical Engineering, Health Polytechnic of Surabaya, Ministry of Health, Indonesia. The role of the Journal is to facilitate contacts between research centers and the industry. The aspiration of the Editors is to publish high-quality scientific professional papers presenting works of significant scientific teams, experienced and well-established authors as well as postgraduate students and beginning researchers. All articles are subject to anonymous review processes by at least two independent expert reviewers prior to publishing on the International Journal of Electronics, Electromedical Engineering, and Medical Informatics website.</p> Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA en-US Journal of Electronics, Electromedical Engineering, and Medical Informatics 2656-8632 <p><strong>Authors who publish with this journal agree to the following terms:</strong></p> <ol> <li class="show">Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlikel 4.0 International <a title="CC BY SA" href="https://creativecommons.org/licenses/by-sa/4.0/" target="_blank" rel="noopener">(CC BY-SA 4.0)</a>&nbsp;that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.</li> <li class="show">Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.</li> <li class="show">Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See&nbsp;<a href="http://opcit.eprints.org/oacitation-biblio.html" target="_new">The Effect of Open Access</a>).</li> </ol> https://mail.jeeemi.org/index.php/jeeemi/article/view/1998 <p><strong>Background</strong>: Diabetic Retinopathy (DR) is a progressive microvascular complication of diabetes and a leading cause of preventable vision impairment worldwide. Early and accurate identification of DR severity is essential for timely intervention and effective disease management. However, conventional retinal screening relies heavily on manual examination of fundus images, which is labor-intensive, subjective, and dependent on specialist expertise. Although recent deep-learning models have demonstrated promising diagnostic performance, many existing approaches inadequately capture lesion-level relationships, underutilize clinical information, and provide limited interpretability for clinical decision-making.</p> <p><strong>Aims</strong>: this study proposes a novel Neuro-Spectral Causal Fusion Network (NSCF-Net) for explainable diabetic retinopathy detection and severity grading.</p> <p><strong>Methods</strong>: The proposed framework integrates an adaptive Neuro-Spectral Enhancement Module to improve lesion visibility, a Lesion Dependency Reasoning Graph to model pathological interactions among retinal abnormalities, and a Dual-Path Hybrid Transformer to jointly learn local lesion characteristics and global retinal context. Furthermore, a Causal Multimodal Fusion Unit combines retinal image representations with clinically relevant parameters, including HbA1c level, blood pressure, age, and duration of diabetes. To improve transparency and clinical trustworthiness, an Explainable Lesion Attribution Module generates counterfactual attention maps and hierarchical saliency visualizations highlighting diagnostically significant retinal regions. The proposed framework was evaluated on the APTOS 2019 diabetic retinopathy dataset, consisting of 3,662 retinal fundus images distributed across five DR severity classes.</p> <p><strong>Results</strong>: Experimental results demonstrate the effectiveness of NSCF-Net, achieving 99.1% accuracy, 98.9% precision, 99.0% recall, and 98.8% F1-score. Comparative analyses and ablation studies further confirm the contribution of neuro-spectral enhancement, lesion dependency reasoning, multimodal fusion, and explainability components to overall performance improvement.</p> <p><strong>Conclusion</strong>: These findings indicate that NSCF-Net provides an accurate, interpretable, and clinically reliable framework for automated diabetic retinopathy screening, severity assessment, and computer-aided ophthalmic decision support.</p> K. Venu B. Thanga Parvathi Kunchanapalli Rama Krishna Yuvaraj S. Copyright (c) 2026 K. Venu, B. Thanga Parvathi, Kunchanapalli Rama Krishna, Yuvaraj S. https://creativecommons.org/licenses/by-sa/4.0 2026-11-04 2026-11-04 9 1 1 14 10.35882/jeeemi.v9i1.1998