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Transfer learning and detecting diabetic retinopathy

In this chapter, using transfer learning, we are going to build a model to detect diabetic retinopathy in the human eye. Diabetic retinopathy is generally found in diabetic patients, where high blood sugar levels cause damage to the blood vessels in the retina. The following image shows a normal retina on the left, and one with diabetic retinopathy on the right:

Figure 2.2: A normal human retina versus a retina with diabetic retinopathy

In healthcare, diabetic retinopathy detection is generally a manual process that involves a trained physician examining color fundus retina images. This introduces a delay in the process of diagnosis, often leading to delayed treatment. As a part of our project, we are going to build a robust artificial intelligence system that can take the color fundus images of the retina and classify the severity of the condition of the retina, with respect to diabetic retinopathy. The different conditions into which we are going to classify the retina images are as follows:

0: No diabetic retinopathy
1: Mild diabetic retinopathy
2: Moderate diabetic retinopathy
3: Severe diabetic retinopathy
4: Proliferative diabetic retinopathy

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