Published Paper Details:

Due to the excessive availability of the fundus cameras, large amount of fundus images are being generated. The fundus images can be utilized by the ophthalmologists to automatically diagnose the retinal diseases. At the same time, the development of artificial intelligence (AI) techniques, especially deep learning (DL) models finds useful for object detection in several application areas, including medical imaging. In this view, this paper presents an automated DL based multi-retinal disease diagnosis and classification model. The presented technique comprises three processes, such as pre-processing, feature extraction, and classification. For feature extraction purposes, AlexNet and Residual Network (ResNet) models are employed. Besides, convolutional neural network (CNN) and deep neural network (DNN) models are applied to classify the retinal fundus images into different retinal diseases. The incorporation of DL models for feature extraction and classification resulted to enhanced diagnostic performance. A series of experiments were conducted on benchmark dataset and the experimental results are examined under different aspects. The experimental values exhibited that the ResNet-CNN model has achieved better performance with the accuracy of 98.84%, precision of 98.84%, recall of 98.17%, and F1-score of 98.48%.