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    Artificial intelligence-based retinal disease classification using optical coherence tomography images

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    Author
    Patnaik, Sohan
    Subasi, Abdulhamit cc
    Subject
    Artificial Intelligence (AI)
    Optical Coherence Tomography (OCT)
    Convolutional Neural Networks (CNN)
    Deep Transfer Learning
    Deep Feature Extraction
    Date
    2023-01-20
    
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    Abstract
    Optical 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.
    Department
    Computer Science
    Publisher
    Academic Press
    Book title
    Applications of Artificial Intelligence in Medical Imaging.
    DOI
    https://doi.org/10.1016/B978-0-443-18450-5.00009-8
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.1016/B978-0-443-18450-5.00009-8
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