  This paper addresses the problem of identifying two main types of lesions -
Exudates and Microaneurysms - caused by Diabetic Retinopathy (DR) in the eyes
of diabetic patients. We make use of Convolutional Neural Networks (CNNs) and
Transfer Learning to locate and generate high-quality segmentation mask for
each instance of the lesion that can be found in the patients' fundus images.
We create our normalized database out of e-ophtha EX and e-ophtha MA and tweak
Mask R-CNN to detect small lesions. Moreover, we employ data augmentation and
the pre-trained weights of ResNet101 to compensate for our small dataset. Our
model achieves promising test mAP of 0.45, altogether showing that it can aid
clinicians and ophthalmologist in the process of detecting and treating the
infamous DR.
