Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING IJID (International Journal on Informatics for Development)
Tinashe Ngwazi
National University of Science and Technology
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Early Detection of Diabetic Retinopathy Through Explainable AI Models: A Systematic Review Tinashe Ngwazi; Belinda Ndlovu; Kudakwashe Maguraushe
IJID (International Journal on Informatics for Development) Vol. 14 No. 2 (2025): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2025.5200

Abstract

Diabetes, if not detected early, can lead to serious complications such as vision loss, known as diabetic retinopathy. Explainable Artificial Intelligence (XAI) can enhance traditional Machine Learning methods, which are not understandable and transparent in diagnostic tasks. This Systematic Literature Review explores data inputs that influence the performance of XAI models in detecting diabetic retinopathy, how XAI techniques can enhance early detection outcomes in diabetic retinopathy, the challenges in implementing these techniques and the ethical implications of using these models in clinical practice. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach guided the search in 4 databases, Springer, Science Direct, PubMed and IEEE Xplore. The findings reveal that XAI techniques like Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP) and Gradient-weighted Class Activation Mapping (GRAD-CAM) offer opportunities like early detection outcomes, integration with existing clinical processes, enhancing trust in AI systems, improving accuracy and personalised treatment. XAI can also facilitate collaboration among clinicians, maintaining fairness in AI systems and supporting adherence to ethical standards. However, research on clinical validation of these models, as well as standardised performance evaluation metrics, is lacking.
Explainable Deep Learning for Diabetic Retinopathy Detection: A Quantitatively Validated Framework Tinashe Ngwazi; Belinda Ndlovu; Kudakwashe Maguraushe
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12687

Abstract

Diabetic retinopathy (DR) is a leading cause of preventable blindness, where early and accurate detection is critical for effective intervention. While deep learning models have demonstrated strong performance in DR classification, their limited interpretability and inconsistent evaluation practices hinder clinical trust and deployment. This study proposes an explainable deep learning framework for DR detection based on MobileNetV2, complemented by Integrated Gradients for feature attribution. A curated dataset of 4,464 retinal images was constructed from publicly available sources through systematic preprocessing, including quality filtering, deduplication, and class balancing across five DR stages. To ensure robust evaluation, a multi-level validation strategy was employed, incorporating stratified train–validation–test splits and k-fold cross-validation. The proposed framework achieved 87.0% accuracy and an F1-score of 0.868, outperforming baseline models including EfficientNet-B0, DenseNet121, and VGG16. Beyond predictive performance, explainability was quantitatively evaluated using deletion and insertion metrics, demonstrating that Integrated Gradients provides more faithful feature attribution compared to Grad-CAM and LIME. Error analysis further reveals that misclassifications are concentrated between adjacent DR stages, reflecting the inherent difficulty of fine-grained disease progression modelling. The findings highlight that combining rigorous validation with quantitative explainability evaluation can improve the reliability and transparency of deep learning models for medical imaging. While results are promising, the framework is validated on publicly available datasets and requires further external clinical validation before real-world deployment.
Co-Authors Belinda Ndlovu Kudakwashe Maguraushe