Diabetic Retinopathy Prediction Based on Wavelet Decomposition and Modified Capsule Network

• Published in: Journal of digital imaging Vol. 36; no. 4; pp. 1739 - 1751
• Main Authors: Oulhadj, Mohammed, Riffi, Jamal, Khodriss, Chaimae, Mahraz, Adnane Mohamed, Bennis, Ahmed, Yahyaouy, Ali, Chraibi, Fouad, Abdellaoui, Meriem, Andaloussi, Idriss Benatiya, Tairi, Hamid
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2023; Springer Nature B.V
• Subjects: Algorithms; Blood vessels; Datasets; Diabetes; Diabetes mellitus; Diabetes Mellitus - pathology; Diabetic retinopathy; Diabetic Retinopathy - diagnostic imaging; Diagnosis, Computer-Assisted; Discrete Wavelet Transform; Fundus Oculi; Humans; Imaging; Medicine; Medicine & Public Health; Radiology; Retina; Retinopathy; Wavelet transforms; Deep learning; Diabetic retinopathy; Discrete wavelet transform; Inception block; Capsule network; Convolutional neural networks; Image classification
• ISSN: 1618-727X; 0897-1889; 1618-727X
• DOI: 10.1007/s10278-023-00813-0

Diabetic retinopathy (DR) is one of the most common consequences of diabetes. It affects the retina, causing blood vessel damage which can lead to loss of vision. Saving patients from losing their sight or at least slowing the progress of this disease depends mainly on the early detection of this pathology, on top of the detection of its specific stage. Furthermore, the early detection of diabetic retinopathy and the follow-up of the patient’s condition remains an arduous task, whether for an experienced expert ophthalmologist or a computer-aided diagnosis technician. In this paper, we aim to propose a new automatic diabetic retinopathy severity level detection method. The proposed approach merges the pyramid hierarchy of the discrete wavelet transform of the retina fundus image with the modified capsule network and the modified inception block proposed, in addition to a new deep hybrid model that concatenates the inception block with capsule networks. The performance of our proposed approach has been validated on the APTOS dataset, as it achieved a high training accuracy of 97.71% and a high testing accuracy score of 86.54%, which is considered one of the best scores achieved in this field using the same dataset.