Deep Transfer Learning Strategy to Diagnose Eye-Related Conditions and Diseases: An Approach Based on Low-Quality Fundus Images.

Data from the World Health Organization indicate that billion cases of visual impairment could be avoided, mainly with regular examinations. However, the absence of specialists in basic health units has resulted in a lack of accurate diagnosis of systemic or asymptomatic eye diseases, increasing the cases of blindness. In this context, the present paper proposes an ensemble of convolutional neural networks, which were submitted to a transfer learning process by using 38,727 high-quality fundus images. Next, the ensemble was tested with 13,000 low-quality fundus images acquired by low-cost equipment. Thus, the proposed approach contributes to advance the state-of-the-art in terms of: (i) validating the proposed transfer learning strategy by recognizing eye-related conditions and diseases in low-quality images; (ii) using high-quality images obtained by high-cost equipment only to train the predictive models; and (iii) reaching results comparable to the state-of-the-art, even using low-quality images. This way, the proposed approach represents a novel deep transfer learning strategy, that is more suitable and feasible to be applied by public health systems of emerging and under-developing countries. From low-quality images, the proposed approach was able to reach accuracies of 87.4%, 90.8%, 87.5%, 79.1% to classify cataract, diabetic retinopathy, excavation and blood vessels, respectively.