Ocular Disease Recognition using Deep Learning
Kuldeep Vayadande, Varad Ingale, Vivek Verma, Abhishek Yeole, Sahil Zawar, Zoya Jamadar
Abstract
Artificial intelligence holds a significant impact in a variety of drug-related medical studies, including ophthalmology. Deep literacy styles, in particular, have been successful in detecting clinical signs and bracketing optical conditions. Studies reveal Ocular diseases to be the major contributing reason of childhood blindness all over the world. Rapid and automatic illness identification is vital and urgent in lowering the strain of ophthalmologists. Ophthalmologists use pattern recognition to identify disorders by looking at the eye and its surrounding tissues directly or indirectly. As a result, can benefit the area of medical greatly. Each disease has several severity levels that can be identified by confirming the presence of different lesions. Morphological characteristics identify each lesion, and numerous lesions from different diseases have similar characteristics. In ophthalmology, deep literacy techniques have mostly been employed on eye fundus pictures and optic consonance tomography. In this paper, we have used three models namely CNN, Inception V3, VGG-19 for cataract prediction. We have got accuracy of 0.9587 for VGG-19 which is performing best as compared to other models.