Abstract
Age-related macular degeneration (AMD) is a macular degenerative disease that is the primary cause of blindness globally. It is often accompanied by choroidal neovascularization (CNV), the abnormal development of blood vessels, which is a typical complication of AMD. Deep Neural Network-based predictors can be used to diagnose retinal illness via machine learning. When it comes to medical difficulties, people still have to rely on the doctor's clarification. Machine learning-based methods have not been highly trusted in the medical industry due to a lack of explanation in neural networks, but there is potential for these methods to be useful in diagnosing retinal illnesses. This study focuses on the classification of Choroidal Neovascularization (CNV) from Optical coherence tomography (OCT) images using efficient fine-tuned ResNet and DenseNet deep learning models. The goal is to accurately identify the presence of CNV in OCT images, which can aid in early diagnosis and treatment of this condition. The models are fine-tuned to improve their performance, and the results are compared to determine the most effective model for CNV classification. The study aims to contribute to the development of more accurate and efficient diagnostic tools for CNV using deep learning technology.
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Goriya, M., Amrutiya, Z., Ghadiya, A., Vasa, J., Patel, B. (2023). Classification of Choroidal Neovascularization (CNV) from Optical Coherence Tomography (OCT) Images Using Efficient Fine-Tuned ResNet and DenseNet Deep Learning Models. In: Choudrie, J., Mahalle, P.N., Perumal, T., Joshi, A. (eds) ICT with Intelligent Applications. ICTIS 2023. Lecture Notes in Networks and Systems, vol 719. Springer, Singapore. https://doi.org/10.1007/978-981-99-3758-5_42
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