Enhanced Lesion Localization and Classification in Ocular Tumor Detection Using Grad-CAM and Transfer Learning

Farhad M.A. Razaque A. Mukhanov S.B. Hassan D.S.M. Mohan Rai H.
2025 Institute of Electrical and Electronics Engineers Inc.

IEEE Access
2025 #13 167762 - 167777 pp.

Ocular tumors pose significant diagnostic challenges due to their rarity and the subtle visual cues they present in fundus images. This paper introduces a novel deep learning framework, termed ELRC-GI (Enhanced Lesion Recognition and Classification with Grad-CAM Integration), and designed for accurate and interpretable ocular tumor detection. The proposed model integrates VGG19 and ResNet50 convolutional neural networks with Grad-CAM-based attention supervision, enabling both high classification accuracy and precise lesion localization. Unlike traditional CNN-based approaches, ELRC-GI incorporates a heatmap-guided loss function that aligns model predictions with interpretable visual explanations, thereby improving clinical trust and diagnostic reliability. We utilize transfer learning by initializing VGG19 and ResNet50 with pre-trained ImageNet weights, freezing the initial layers and fine-tuning the final layers using the RFMiD dataset to adapt the models to the ocular tumor detection task. The model maintains a true positive rate of 96% at a false positive rate of 0.3%, as evidenced by a robust ROC curve. Experimental results on the RFMiD dataset demonstrate the superiority of ELRC-GI, achieving 97% accuracy, 93% precision, 85% recall, and an AUC of 0.98, significantly outperforming baseline CNN models. Grad-CAM visualizations further validate the model’s capability to highlight tumor regions, even in the presence of overlapping ocular conditions. The ELRC-GI model thus offers a robust, explainable, and clinically viable solution for early ocular tumor detection, setting the stage for broader application in interpretable medical AI.

Deep learning , explainable AI , fundus imaging , Grad-CAM , lesion localization , ocular tumor detection , transfer learning

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International Information Technology University, Department of Computer Engineering, Almaty, 050000, Kazakhstan
Arkansas Tech University, Department of Computer Science and Information Sciences, Russellville, 72801, AR, United States
Princess Nourah bint Abdulrahman University, College of Computer and Information Sciences, Department of Information Technology, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
Nazarbayev University, Department of Computer Science, Almaty, 050000, Kazakhstan

International Information Technology University
Arkansas Tech University
Princess Nourah bint Abdulrahman University
Nazarbayev University

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026