Eye Disease Detection Portal

Abstract

Eye diseases and cancer, affecting millions of people in the developing world, can lead to vision loss. Tomography, a type of X-ray technique, is used for their detection, but symptoms like pain, blurriness, and redness may go unnoticed. Limited access to expertise in metropolitan areas poses a challenge for accurate diagnosis, despite the availability of scanning centers in many towns. By utilizing Deep Learning techniques, we have revolutionized the detection of eye diseases and cancer. This project involves extensive datasets obtained from previously scanned tomography scans, which are subjected to preprocessing steps to ensure optimal quality. The trained models learn from these datasets, enabling them to accurately classify eye conditions. To facilitate ease of use, we have developed an intuitive interface that allows ophthalmologists to input eye images from scans. Leveraging the power of Deep Learning algorithms, the system swiftly analyzes the images and generates comprehensive reports indicating the presence of various eye diseases and cancer. This approach addresses the limitations of traditional diagnostic methods, as it significantly reduces the time and effort required for disease identification. By incorporating advanced Deep Learning techniques, our system achieves the highest levels of accuracy in detecting and diagnosing eye diseases, ensuring prompt and effective treatment for patients. Overall, our project showcases the potential of Deep Learning in revolutionizing the detection and diagnosis of eye diseases and cancer, ensuring that patients receive prompt and accurate treatment regardless of their geographical location.