Patnaik, SohanSubasi, Abdulhamit2023-03-132023-03-132023-01-20Sohan Patnaik, Abdulhamit Subasi, Chapter 12 - Artificial intelligence-based retinal disease classification using optical coherence tomography images, Editor(s): Abdulhamit Subasi, In Artificial Intelligence Applications in Healthcare&Medicine, Applications of Artificial Intelligence in Medical Imaging, Academic Press, 2023, Pages 305-319, ISBN 9780443184505, https://doi.org/10.1016/B978-0-443-18450-5.00009-8. (https://www.sciencedirect.com/science/article/pii/B9780443184505000098)9780443184505https://doi.org/10.1016/B978-0-443-18450-5.00009-8http://hdl.handle.net/20.500.14131/631Optical coherence tomography (OCT) is a noninvasive imaging technology used to obtain high-resolution cross-sectional images of the retina. The layers within the retina can be differentiated and retinal thickness can be measured to aid in the early detection and diagnosis of retinal diseases and conditions. Notwithstanding the proven utility of OCT images, diagnosing large datasets of OCT images using the manual method still remains a challenge. In this chapter, we propose a deep learning-based approach, namely, the use of convolutional neural networks (CNN) and some pretrained image classification models on top of CNNs to get a proper and faster diagnosis of the OCT images. We also experiment with the features extracted using pretrained image classification models. Mainly three diseases—drusen, diabetic macular edema, choroidal neovascularization are addressed in this study. Our technique achieves an accuracy score of 0.9948 and an F1 score of 0.9948 on the test set. The outcomes suggest that the proposed model could serve as an analytic module to alert users and/or medical experts when retinal diseases are suspected.Artificial Intelligence (AI)Optical Coherence Tomography (OCT)Convolutional Neural Networks (CNN)Deep Transfer LearningDeep Feature ExtractionHEALTH