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

TL;DR

This paper surveys deep learning methods for detecting ocular diseases from fundus images, analyzing their architectures, environments, and performance metrics to evaluate their effectiveness in multi-class pathology classification.

Contribution

It provides a comprehensive review of existing deep learning approaches for ocular pathology detection, including processing steps, neural network structures, and evaluation practices.

Findings

Deep learning methods show higher detection performance.

Performance ratios and execution times vary across methods.

Analysis of hardware/software environments used in studies.

Abstract

Ocular pathology detection from fundus images presents an important challenge on health care. In fact, each pathology has different severity stages that may be deduced by verifying the existence of specific lesions. Each lesion is characterized by morphological features. Moreover, several lesions of different pathologies have similar features. We note that patient may be affected simultaneously by several pathologies. Consequently, the ocular pathology detection presents a multi-class classification with a complex resolution principle. Several detection methods of ocular pathologies from fundus images have been proposed. The methods based on deep learning are distinguished by higher performance detection, due to their capability to configure the network with respect to the detection objective. This work proposes a survey of ocular pathology detection methods based on deep learning. First, we study the existing methods either for lesion segmentation or pathology classification. Afterwards, we extract the principle steps of processing and we analyze the proposed neural network structures. Subsequently, we identify the hardware and software environment required to employ the deep learning architecture. Thereafter, we investigate about the experimentation principles involved to evaluate the methods and the databases used either for training and testing phases. The detection performance ratios and execution times are also reported and discussed.