Unified Hybrid Inception Using Hybrid Neural Encoder-Decoder for AMD Detection on Retinal Fundus Images

Abstract

Age-related macular degeneration (AMD), a common eye illness that can cause significant vision loss in older persons, can also result in visual loss. It is a medical issue that is primarily brought on by aging. In this paper, a combinative method is presented using U-Net with a modified Inception architecture for the diagnosis of AMD disease. The proposed method is based on deep neural architecture formalizing encoder decoder modeling with convolutional architectures, namely Inception and Residual Connection. The performance of the proposed model was validated on the ADAM AMD Dataset. Experiments demonstrate that the modified Inception deep feature extractor improves AMD classification with a classification accuracy of 92.5 % and segmentation accuracy of 99 % in ADAM across classes in comparison to Resnet. The paper tests the dataset with the proposed model of Hybrid Dense-ED-UHI: Encoder-Decoder based Unet Hybrid Inception model with 15-fold cross-validation. The paper, in detail, discusses the various metrics of the proposed model with various visualizations and multifold validations.