"Health Scan" is a groundbreaking Android application that harnesses the power of technology to make cataract detection accessible and user-friendly. By using a sophisticated tech stack that includes Java, Kotlin, XML, Android, and Tensorflow Lite, this project achieves a remarkable 92% accuracy rate in cataract detection. The app allows users to self-assess their eye health by capturing images through their device's camera, instantly detecting cataract and normal eye conditions.
Key Achievements:
MedNet Model Development: This project involved the development of a MedNet model, a neural network tailored for medical image analysis. The model was meticulously trained using a comprehensive dataset of cataract eye images. Through rigorous fine-tuning, it achieved an exceptional accuracy rate of 92%, ensuring precise cataract detection.
Tensorflow Lite Integration: The fine-tuned MedNet model was seamlessly integrated into the Android application using Tensorflow Lite. This integration is a testament to the team's technical prowess and dedication. It enables real-time detection of cataract and normal eye conditions from images captured by the device's camera.
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Impact: "Health Scan" is not just another mobile application; it is a game-changer in eye health. By putting the power of cataract detection in the hands of users, the app offers several significant benefits:
Early Detection: Cataracts are one of the leading causes of vision impairment and blindness. With early detection through "Health Scan," users can seek timely medical intervention, potentially preserving their vision.
Accessibility: The user-friendly design and real-time results make cataract screening convenient and accessible to a wide audience, including those in remote or underserved areas.
Empowerment: Users gain a sense of control over their eye health, making informed decisions about seeking professional care.
Future Potential: The success of "Health Scan" is a testament to the potential of mobile technology, artificial intelligence, and medical diagnostics. This project not only addresses the critical issue of cataract detection but also paves the way for similar apps and solutions aimed at detecting other health-related conditions.