@Article{cmc.2025.061018,
AUTHOR = {Pooja Bidwai, Shilpa Gite, Biswajeet Pradhan, Abdullah Almari},
TITLE = {Explainable Diabetic Retinopathy Detection Using a Distributed CNN and LightGBM Framework},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {84},
YEAR = {2025},
NUMBER = {2},
PAGES = {2645--2676},
URL = {http://www.techscience.com/cmc/v84n2/62859},
ISSN = {1546-2226},
ABSTRACT = {Diabetic Retinopathy (DR) is a critical disorder that affects the retina due to the constant rise in diabetics and remains the major cause of blindness across the world. Early detection and timely treatment are essential to mitigate the effects of DR, such as retinal damage and vision impairment. Several conventional approaches have been proposed to detect DR early and accurately, but they are limited by data imbalance, interpretability, overfitting, convergence time, and other issues. To address these drawbacks and improve DR detection accurately, a distributed Explainable Convolutional Neural network-enabled Light Gradient Boosting Machine (DE-ExLNN) is proposed in this research. The model combines an explainable Convolutional Neural Network (CNN) and Light Gradient Boosting Machine (LightGBM), achieving highly accurate outcomes in DR detection. LightGBM serves as the detection model, and the inclusion of an explainable CNN addresses issues that conventional CNN classifiers could not resolve. A custom dataset was created for this research, containing both fundus and OCTA images collected from a real-time environment, providing more accurate results compared to standard conventional DR datasets. The custom dataset demonstrates notable accuracy, sensitivity, specificity, and Matthews Correlation Coefficient (MCC) scores, underscoring the effectiveness of this approach. Evaluations against other standard datasets achieved an accuracy of 93.94%, sensitivity of 93.90%, specificity of 93.99%, and MCC of 93.88% for fundus images. For OCTA images, the results obtained an accuracy of 95.30%, sensitivity of 95.50%, specificity of 95.09%, and MCC of 95%. Results prove that the combination of explainable CNN and LightGBM outperforms other methods. The inclusion of distributed learning enhances the model’s efficiency by reducing time consumption and complexity while facilitating feature extraction.},
DOI = {10.32604/cmc.2025.061018}
}