Harnessing the Strength of ResNet50 to Improve the Ocular Disease Recognition
Gunjan Sharma, Vatsala Anand, Sheifali Gupta
Abstract
Among the most prevalent eye conditions, cataract is the leading cause of blindness, impairing vision. A cataract is a condition that means clouding of the lens of the eye. Cataract-related blindness can be mainly prevented with early detection and prompt treatment. Artificial intelligence systems that grade cataracts based on fundus pictures are a practical way to help clinicians detect cataracts more accurately. For early detection of cataracts, Convolutional neural networks, also referred to as CNNs, have been reported to have a great deal of promise in several different domains, including the identification of many eye illnesses. In this research, a deep CNN model based on ResNet50 architecture has been proposed to classify the images into cataract-infected and normal classes. For fulfilling this task Ocular Disease Intelligent Recognition dataset has been chosen. This dataset contains real-time patient reports of both eyes. The model has shown a very good accuracy of 95.63% and 90.37% of validation accuracy while using SGD optimizer. The loss was 0.64 which is nominal and this model has shown very promising results in classifying the images. This model has a very innovative approach in the medical field so it can be used as a tool in the biomedical or healthcare field.