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Ocular diseases diagnosis in fundus images using a deep learning: approaches, tools and performance evaluation
Author(s): Yaroub Elloumi; Mohamed Akil; Henda Boudegga
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Paper 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 multiclass 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.

Paper Details

Date Published: 14 May 2019
PDF: 8 pages
Proc. SPIE 10996, Real-Time Image Processing and Deep Learning 2019, 109960T (14 May 2019); doi: 10.1117/12.2519098
Show Author Affiliations
Yaroub Elloumi, Gaspad Monge Computer Science Lab., ESIEE-Paris, Univ. Paris-Est (France)
Univ. of Monastir (Tunisia)
Univ. of Sousse (Tunisia)
Mohamed Akil, Gaspad Monge Computer Science Lab., ESIEE-Paris, Univ. Paris-Est (France)
Henda Boudegga, Univ. de Monastir (Tunisia)


Published in SPIE Proceedings Vol. 10996:
Real-Time Image Processing and Deep Learning 2019
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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