Convolutional Neural Network Model for Diabetic Retinopathy Feature
  Extraction and Classification
Sharan Subramanian Leilani H. Gilpin
The application of Artificial Intelligence in the medical market brings up
increasing concerns but aids in more timely diagnosis of silent progressing
diseases like Diabetic Retinopathy. In order to diagnose Diabetic Retinopathy
(DR), ophthalmologists use color fundus images, or pictures of the back of the
retina, to identify small distinct features through a difficult and
time-consuming process. Our work creates a novel CNN model and identifies the
severity of DR through fundus image input. We classified 4 known DR features,
including micro-aneurysms, cotton wools, exudates, and hemorrhages, through
convolutional layers and were able to provide an accurate diagnostic without
additional user input. The proposed model is more interpretable and robust to
overfitting. We present initial results with a sensitivity of 97% and an
accuracy of 71%. Our contribution is an interpretable model with similar
accuracy to more complex models. With that, our model advances the field of DR
detection and proves to be a key step towards AI-focused medical diagnosis.