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Ocular pathology detection from fundus images presents an important challengeon health care. In fact, each pathology has different severity stages that maybe deduced by verifying the existence of specific lesions. Each lesion ischaracterized by morphological features. Moreover, several lesions of differentpathologies have similar features. We note that patient may be affectedsimultaneously by several pathologies. Consequently, the ocular pathologydetection presents a multi-class classification with a complex resolutionprinciple. Several detection methods of ocular pathologies from fundus imageshave been proposed. The methods based on deep learning are distinguished byhigher performance detection, due to their capability to configure the networkwith respect to the detection objective. This work proposes a survey of ocularpathology detection methods based on deep learning. First, we study theexisting methods either for lesion segmentation or pathology classification.Afterwards, we extract the principle steps of processing and we analyze theproposed neural network structures. Subsequently, we identify the hardware andsoftware environment required to employ the deep learning architecture.Thereafter, we investigate about the experimentation principles involved toevaluate the methods and the databases used either for training and testingphases. The detection performance ratios and execution times are also reportedand discussed.

Ocular Diseases Diagnosis in Fundus Images using a Deep Learning: Approaches, tools and Performance evaluation