The goal of this repository is to use the competition data to build machine learning models to process the image data and forecast if the image has any degree of diabetic retinopathy, also try techniques of image preprocessing to help the models extract relevant features.
- Documentation [link]
- Project working cycle and effort, relevant content and insights [link]
- Best solution report [link]
- Dataset split [link]
- Models [link]
Kaggle competition: https://www.kaggle.com/c/aptos2019-blindness-detection/overview
Imagine being able to detect blindness before it happened.
Millions of people suffer from diabetic retinopathy, the leading cause of blindness among working aged adults. Aravind Eye Hospital in India hopes to detect and prevent this disease among people living in rural areas where medical screening is difficult to conduct. Successful entries in this competition will improve the hospital’s ability to identify potential patients. Further, the solutions will be spread to other Ophthalmologists through the 4th Asia Pacific Tele-Ophthalmology Society (APTOS) Symposium
Currently, Aravind technicians travel to these rural areas to capture images and then rely on highly trained doctors to review the images and provide diagnosis. Their goal is to scale their efforts through technology; to gain the ability to automatically screen images for disease and provide information on how severe the condition may be.
In this synchronous Kernels-only competition, you'll build a machine learning model to speed up disease detection. You’ll work with thousands of images collected in rural areas to help identify diabetic retinopathy automatically. If successful, you will not only help to prevent lifelong blindness, but these models may be used to detect other sorts of diseases in the future, like glaucoma and macular degeneration.
Get started today!