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Communication Dans Un Congrès Année : 2023

Retinal pathologies detection in OCT images based on Bilinear convolutional neural network

Résumé

Retinal pathologies like choroidal neovascularization (CNV), drusen, and diabetic macular edema (DME) can give rise to microvascular alterations in the retina, ultimately resulting in vision impairment. The manual detection of these diseases poses a significant challenge and necessitates specialized medical expertise. To address this challenge, our study introduces novel deep learning methods for the detection of these ocular pathologies automatically and based on optical coherence tomography (OCT) scans. In our experimental setup, we utilized a dataset comprising 6000 OCT images sourced from the publicly available Kaggle dataset. Through comprehensive evaluations, our study revealed that the implementation of a bilinear convolutional neural network (B-CNN) yielded the highest classification score, surpassing the accuracy achieved by alternative models. Furthermore, when compared to other deep learning networks, our proposed approach showcased superior performance in the early diagnosis of these three ocular diseases.
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Dates et versions

hal-04360939 , version 1 (22-12-2023)

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Zainab Haddad, Brahim Mahamat Yaya, Hsouna Zgolli, Désiré Sidibé, Hedi Tabia, et al.. Retinal pathologies detection in OCT images based on Bilinear convolutional neural network. 17th International Conference on Innovations in Intelligent Systems and Applications (INISTA 2023), Sep 2023, Hammamet, Tunisia. pp.1-7, ⟨10.1109/INISTA59065.2023.10310341⟩. ⟨hal-04360939⟩
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