1. Evaluation of Transfer Learning for Classification of: (1) Diabetic Retinopathy by Digital Fundus Photography and (2) Diabetic Macular Edema, Choroidal Neovascularization and Drusen by Optical Coherence Tomography (Thesis)

    Evaluation of Transfer Learning for Classification of: (1) Diabetic Retinopathy by Digital Fundus Photography and (2) Diabetic Macular Edema, Choroidal Neovascularization and Drusen by Optical Coherence Tomography (Thesis)

    Deep learning has been successfully applied to a variety of image classification tasks. In the last few years, starting with the ground-breaking results of AlexNet at the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), there has been tremendous and rapid growth in deep learning and its potential applications. There has been keen interest to apply deep learning in the medical domain, particularly specialties that heavily utilize imaging, such as dermatology, pathology, radiology, and ophthalmology. One issue that may hinder application of deep learning to the medical domain is the vast amount of data necessary to train deep neural networks (DNNs ...

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