Diabetic Retinopathy Classification and Interpretation using Deep Learning Techniques

Identificador:  TDX:2930

Handle: https://hdl.handle.net/20.500.11797/TDX2930

Autors:  De la Torre Gallart, Jordi

Resum:
Diabetic Retinopathy is a chronic disease and one of the main causes of blindness and visual impairment for diabetic patients. Eye screening through retinal images is used by physicians to detect the lesions related with this disease. In this thesis, we explore different novel methods for the automatic diabetic retinopathy disease grade classification using retina fundus images. For this purpose, we explore methods based in automatic feature extraction and classification, based on deep neural networks. Furthermore, as results reported by these models are difficult to interpret, we design a new method for results interpretation. The model is designed in a modular manner in order to generalize its possible application to other networks and classification domains. We experimentally demonstrate that our interpretation model is able to detect retina lesions in the image solely from the classification information. Additionally, we propose a method for compressing model feature-space information. The method is based on a independent component analysis over the disentangled feature space information generated by the model for each image and serves also for identifying the mathematically independent elements causing the disease. Using our previously mentioned interpretation method is also possible to visualize such components on the image. Finally, we present an experimental application of our best model for classifying retina images of a different population, concretely from the Hospital de Reus. The methods proposed, achieve ophthalmologist performance level and are able to identify with great detail lesions present on images, inferred only from image classification information.