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An improved approach for classification of glaucoma stages from color fundus images using Efficientnet-b0 convolutional neural network and recurrent neural network by Elangovan, Poonguzhali (author);Vijayalakshmi, D. (author);Nath, Malaya Kumar (author) is a scholarly article available to read on EtoBox.

What is An improved approach for classification of glaucoma stages from color fundus images using Efficientnet-b0 convolutional neural network and recurrent neural network about?

Glaucoma is a cluster of eye diseases caused due to the deterioration of the optic nerve. It affects tunnel vision at the early stages and progresses gradually without easily noticeable symptoms. Hence, accurate diagnosis and timely treatment are essential to prevent vision loss. The noninvasive and cost-effective nature of the fundus imaging modality makes it more appropriate for the diagnosis of glaucoma stages. However, manual interpretation of optic nerve head examinations requires skilled experts and is also time consuming. Recently, deep learning models have provided superior performance in the glaucoma classification tasks. This paper presents a unique approach for the classification of glaucoma stages (such as mild, moderate, and advanced) from fundus images. The proposed approach includes fine-tuning the Efficientnet-b0 model for extracting the discriminative features, Haar wavelet for feature reduction, and bidirectional long short-term memory network (Bi-LSTM) for final classification. The efficacy of the proposed model is evaluated with 16,345 advanced glaucoma images, 16,184 early glaucoma images, and 16,584 normal images. Experiment results reveal that the classificat

Author

Elangovan, Poonguzhali (author);Vijayalakshmi, D. (author);Nath, Malaya Kumar (author)

Publisher

Elsevier Science & Technology

Published

2023

Language

EN

ISBN

9780323954150