Authors:
O. Jeba Singh, S. Rubin Bose, J. Angelin Jeba
Addresses:
Center for Academic Research, Alliance University, Bengaluru, Karnataka, India. School of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. Department of Electronics and Communication Engineering, S.A. Engineering College, Chennai, Tamil Nadu, India.
Abstract:
Diabetic Retinopathy (DR) is a significant vision-threatening condition in diabetic individuals, mostly caused by damage to the retinal blood vessels. Microaneurysms, small red patches on the retina, are among the first indicators of DR. If left untreated, they can lead to serious vision problems or even blindness. Consequently, early and precise identification of microaneurysms is essential for efficient treatment and the prevention of disease progression. This research introduces an innovative deep learning framework for the automatic identification of microaneurysms in retinal fundus images with a Frozen-based VGG19 model. The suggested method has two main steps: pre-processing and classification. During preprocessing, lighting changes are corrected, and image scaling is applied to facilitate feature extraction. The modified Frozen-based VGG19 model is used in the classification phase to quickly identify microaneurysms without segmenting vessels. We tested the proposed technique on three benchmark datasets: E-ophtha, ROC, and DIARETDB1. We also compared it with existing transfer learning architectures such as VGG16 and VGG19. The experimental results show that the proposed model achieves 93.4% detection accuracy on the E-ophtha dataset, 88.3% on the ROC dataset, and 83.5% on the DIARETDB1 dataset. The work emphasises the capabilities of sophisticated deep learning models for early diagnosis of diabetic retinopathy, facilitating prompt clinical intervention and improving patient outcomes.
Keywords: Diabetic Retinopathy (DR); Transfer Learning; Benchmark Datasets; Fundus Images; Deep Learning; Enhancing Patient Outcomes; Feature Extraction; Blood Vessels.
Received: 20/09/2024, Revised: 07/01/2025, Accepted: 10/03/2025, Published: 05/06/2025
DOI: 10.64091/ATIHL.2025.000170
AVE Trends in Intelligent Health Letters, 2025 Vol. 2 No. 2 , Pages: 108-116