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![Computational Methods and Deep Learning for Ophthalmology](/uploads/posts/2023-03/1678721247_2111_computational_m_thods_and_d__p_l_arning_for_ophthalmology.jpg)
Автор: D. Jude Hemanth
Издательство: Academic Press/Elsevier
Год: 2023
Страниц: 354
Язык: английский
Формат: pdf
Размер: 17.9 MB
Computational Methods and Deep Learning for Ophthalmology presents readers with the concepts and methods needed to design and use advanced computer-aided diagnosis systems for ophthalmologic abnormalities in the human eye. Chapters cover computational approaches for diagnosis and assessment of a variety of ophthalmologic abnormalities. Computational approaches include topics such as Deep Convolutional Neural Networks, Generative Adversarial Networks, Auto Encoders, Recurrent Neural Networks, and modified/hybrid Artificial Neural Networks. Ophthalmological abnormalities covered include Glaucoma, Diabetic Retinopathy, Macular Degeneration, Retinal Vein Occlusions, eye lesions, cataracts, and optical nerve disorders. This study explores the classification process using three CNNs, CifarNet, AlexNet, and GoogLeNet. The CNNs are examined for two different diseases Thoraco-abdominal lymph node and interstitial lung disease classification. This study shows that limited datasets can cause bottleneck problems. Therefore, large-scale annotated datasets can be beneficially classified using transfer learning models.