Problem description
Diabetic retinopathy is the leading cause of blindness in the working-age population of the developed world. It is estimated to affect over 93 million people.
The US Center for Disease Control and Prevention estimates that 29.1 million people in the US have diabetes and the World Health Organization estimates that 347 million people have the disease worldwide. Diabetic Retinopathy (DR) is an eye disease associated with long-standing diabetes. Around 40% to 45% of Americans with diabetes have some stage of the disease. Progression to vision impairment can be slowed or averted if DR is detected in time, however this can be difficult as the disease often shows few symptoms until it is too late to provide effective treatment.
Currently, detecting DR is a time-consuming and manual process that requires a trained clinician to examine and evaluate digital colour fundus photographs of the retina. By the time human readers submit their reviews, often a day or two later, the delayed results lead to lost follow up, miscommunication, and delayed treatment.
Clinicians can identify DR by the presence of lesions associated with the vascular abnormalities caused by the disease. While this approach is effective, its resource demands are high. The expertise and equipment required are often lacking in areas where the rate of diabetes in local populations is high and DR detection is most needed. As the number of individuals with diabetes continues to grow, the infrastructure needed to prevent blindness due to DR will become even more insufficient.
The need for a comprehensive and automated method of DR screening has long been recognized, and previous efforts have made good progress using image classification, pattern recognition, and machine learning.
Problem source (URL)
Scanning medical images of eyes to detect which patients might be at risk of going blind
https://www.kaggle.com/competitions/diabetic-retinopathy-detection/overview
Citation:
Emma Dugas, Jared, Jorge, Will Cukierski. (2015). Diabetic Retinopathy Detection. Kaggle. https://kaggle.com/competitions/diabetic-retinopathy-detection
Codebase description
This codebase utilises deep learning to grade the severity of diabetic retinopathy, which can help with accelerated detection and treatment. It uses transfer learning, oversampling, and progressive resizing to work with small and imbalanced datasets and large images.
Codebase source (URL)
https://www.kaggle.com/code/tanlikesmath/diabetic-retinopathy-with-resnet50-oversampling/notebook
Dataset description
A large set of high-resolution retina images taken under a variety of imaging conditions. A left and right field is provided for every subject. Images are labelled with a subject id as well as either left or right (e.g. 1_left.jpeg is the left eye of patient id 1).
A clinician has rated the presence of diabetic retinopathy in each image on a scale of 0 to 4, according to the following scale:
0 - No DR
1 - Mild
2 - Moderate
3 - Severe
4 - Proliferative DR
Dataset source (URL)
https://www.kaggle.com/competitions/diabetic-retinopathy-detection/data
