Machine learning for retinal health classification of optical coherence tomography images
Version 1Optical Coherence Tomography (OCT) is an essential imaging technique for diagnosing retinal diseases. While deep learning models offer high accuracy for automated OCT classification, their “black-box” nature limits clinical trust and adoption. Conversely, traditional machine learning methods are more interpretable but often depend on features that can be difficult to extract or are sensitive to image quality. This thesis presents two complementary studies to develop an accurate and interpretable
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Optical Coherence Tomography (OCT) is an essential imaging technique for diagnosing retinal diseases. While deep learning models offer high accuracy for automated OCT classification, their “black-box” nature limits clinical trust and adoption. Conversely, traditional machine learning methods are more interpretable but often depend on features that can be difficult to extract or are sensitive to image quality. This thesis presents two complementary studies to develop an accurate and interpretable