Kachi Anvesh1,2, Bharati M. Reshmi2,3, Shanmugasundaram Hariharan4, H. Venkateshwara Reddy5, Murugaperumal Krishnamoorthy6, Vinay Kukreja7, Shih-Yu Chen8,9,*
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1485-1517, 2025, DOI:10.32604/cmes.2025.063239
- 30 May 2025
Abstract Automated classification of retinal fundus images is essential for identifying eye diseases, though there is earlier research on applying deep learning models designed especially for detecting tessellation in retinal fundus images. This study classifies 4 classes of retinal fundus images with 3 diseased fundus images and 1 normal fundus image, by creating a refined VGG16 model to categorize fundus pictures into tessellated, normal, myopia, and choroidal neovascularization groups. The approach utilizes a VGG16 architecture that has been altered with unique fully connected layers and regularization using dropouts, along with data augmentation techniques (rotation, flip, and… More >