Multi source retinal fundus image classification using convolution neural networks fusion and Gabor-based texture representation

Glaucoma is one of the most known irreversible chronic eye disease that leads to permanent blindness but its earlier diagnosis can be treated. Convolutional neural networks (CNNs), a branch of deep learning, have an impressive record for applications in image analysis and interpretation, including medical imaging. This necessity is justified by their capacity and adaptability to extract pertinent features automatically from the original image. In other hand, the use of ensemble learning algorithms has an important impact to improve the classification rate. In this paper, a two-stage-based image processing and ensemble learning approach is proposed for automated glaucoma diagnosis. In the first stage, the generation of different modalities from original images is adopted by the application of advanced image processing techniques especially Gabor filter-based texture image. Next, each dataset constructing from the corresponding modality will be learned by an individual CNN classifier. Aggregation techniques will be then applied to generate the final decision taking into account the outputs of all CNNs classifiers. Experiments were carried out on Rime-One dataset for glaucoma diagnosis. The obtained results proved the superiority of the proposed ensemble learning system compared to the existing studies with classification accuracy of 89.63%