Detection of Retinal Abnormalities in Fundus Image Using CNN Deep Learning Networks

Mohamed Akil (1) , Yaroub Elloumi (2, 1) , Rostom Kachouri (1)

1 LIGM - Laboratoire d'Informatique Gaspard-Monge
2 TIM - Laboratoire Technologie et Imagerie Médicale [Monastir]

The World Health Organization estimates that 285 million people are visually impaired worldwide, with 39 million blinds. Glaucoma, Cataract, Age-related macular degeneration, Diabetic retinopathy are among the leading retinal diseases. Thus, there is an active effort to create and develop methods to automate screening of retinal diseases. Many Computer Aided Diagnosis systems for ocular diseases have been developed and are widely used. Deep learning-based Artificial Intelligence has shown its abilities in different medical domains including ophthalmology. Deep learning and convolutional neural networks are able to identify, localize and quantify pathological features and retinal disease and their performance keeps growing. In this chapter, we will present an overview of the used CNN Deep Learning-based methods in detection of retinal abnormalities related to the most severe ocular diseases in retinal images, where network architectures, post/preprocessing and evaluation experiments are detailed. We also present some related work concerning the Deep Learning-based Smartphone applications for earlier screening and diagnosisof retinal diseases.

Mots clés: Smartphone applications; classification; detection; Ocular diseases screening; Convolutional Neural Networks; Deep Learning; diagnosis

Domaines: Intelligence artificielle [cs.AI]; Imagerie médicale; Traitement des images [eess.IV]

Mohamed Akil, Yaroub Elloumi, Rostom Kachouri. Detection of Retinal Abnormalities in Fundus Image Using CNN Deep Learning Networks. Elsevier. State of the Art in Neural Networks, 1, Ayman S. El-Baz; Jasjit S. Suri, pp.19-61, 2021. ⟨hal-02428351⟩