Convolutional Neural Network Model for Diabetic Retinopathy Feature Extraction and Classification
This work addresses the time-consuming and difficult process of manual DR diagnosis for ophthalmologists, though it appears incremental as it focuses on interpretability with similar accuracy to existing models.
The paper tackled the problem of diagnosing Diabetic Retinopathy by developing a CNN model to classify four key features from fundus images, achieving a sensitivity of 97% and accuracy of 71% for automated diagnosis without user input.
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.