Diabetic Retinopathy Classification Using an Efficient Convolutional Neural Network

Diabetic Retinopathy (DR) is a diabetic complication that affects the eyes and may lead to blurred vision or even blindness. The diagnosis of DR through eye fundus images is traditionally performed by ophthalmologists who inspect for the presence and significance of many subtle features, a process which is cumbersome and time-consuming. As there are many undiagnosed and untreated cases of DR, DR screening of all diabetic patients is a huge challenge. Some previous works have applied deep convolutional neural networks(CNNs) to detect DR automatically. However, these methods employed very deep CNNs which require extensive computational resources. In this paper, we proposed a computationally efficient classification system based on efficient CNNs. Our results show that the proposed method achieves or surpasses state-of-the-art methods on two commonly used DR datasets.