Glaucoma and Cataract Detection

Project Code :TCMAPY1083

Objective

The objective of this study is to enhance glaucoma and cataract detection through the implementation of deep learning algorithms, specifically Convolutional Neural Networks (CNN) and MobileNet. Leveraging these advanced algorithms, our goal is to develop an accurate and efficient automated system for early detection and classification of glaucoma and cataracts from medical imaging data. By employing CNN and MobileNet architectures, we aim to improve diagnostic precision, facilitate timely intervention, and contribute to the overall effectiveness of ophthalmic healthcare.

Abstract

It presents a novel approach for glaucoma and cataract detection utilizing deep learning, specifically employing Convolutional Neural Network (CNN) and MobileNet algorithms. The proposed method leverages the power of these algorithms to analyze ocular images and accurately identify early signs of glaucoma and cataracts. Through extensive training on diverse datasets, our model demonstrates high sensitivity and specificity. This automated system holds great potential for early diagnosis, enabling timely intervention and preventing irreversible vision loss. The integration of CNN and MobileNet showcases the efficiency of deep learning in enhancing diagnostic accuracy for ocular diseases, fostering advancements in telemedicine and public health.

Keywords: Glaucoma and cataract dataset, CNN, Mobile net algorithm and etc.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.