Diabetic retinopathy (DR) is the result of a complication of diabetes affecting the retina. It can cause blindness, if left undiagnosed and untreated. An ophthalmologist performs the diagnosis by screening each patient and analyzing the retinal lesions via ocular imaging. In practice, such analysis is time-consuming and cumbersome to perform. This paper presents a model for automatic DR classification on eye fundus images. The approach identifies the main ocular lesions related to DR and subsequently diagnoses the illness. The proposed method follows the same workflow as the clinicians, providing information that can be interpreted clinically to support the prediction. A subset of the kaggle EyePACS and the Messidor-2 datasets, labeled with ocular lesions, is made publicly available. The kaggle EyePACS subset is used as training set and the Messidor-2 as a test set for lesions and DR classification models. For DR diagnosis, our model has an area-under-the-curve, sensitivity, and specificity of 0.948, 0.886, and 0.875, respectively, which competes with state-of-the-art approaches.
Age-related macular degeneration is a common cause of vision loss in people aging 55 and older. The condition affects the light-sensing cells in the macula limiting the sharp and central vision. On the other hand, Spectral Domain Optical Coherence Tomography (SD-OCT) allow highlighting abnormalities and thickness in the retinal layers which are useful for age-related macular degeneration diagnosis and follow up. The Neurosensory retina (NSR) map is defined as the thickness between the inner limiting membrane layer and the inner aspect of the retinal pigment epithelium complex. Additionally, the NSR map has been used to differentiate between healthy and subjects with macular problems, but the plotting of the retinal thickness map depends critically on additional manufacturer interpretation software to automatically drawing. Therefore, this paper presents an end-to-end 3D convolutional neural network to automatically extract nine thickness mean values to draw the NSR map from an SD-OCT.
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