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Classification of Glaucoma in Retinal Images Using EfficientnetB4 Deep Learning Model

A. Geetha, N. B. Prakash*

National Engineering College, Kovilpatti, 628502, India

* Corresponding Author: N. B. Prakash. Email: email

Computer Systems Science and Engineering 2022, 43(3), 1041-1055. https://doi.org/10.32604/csse.2022.023680

Abstract

Today, many eye diseases jeopardize our everyday lives, such as Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), and Glaucoma. Glaucoma is an incurable and unavoidable eye disease that damages the vision of optic nerves and quality of life. Classification of Glaucoma has been an active field of research for the past ten years. Several approaches for Glaucoma classification are established, beginning with conventional segmentation methods and feature-extraction to deep-learning techniques such as Convolution Neural Networks (CNN). In contrast, CNN classifies the input images directly using tuned parameters of convolution and pooling layers by extracting features. But, the volume of training datasets determines the performance of the CNN; the model trained with small datasets, overfit issues arise. CNN has therefore developed with transfer learning. The primary aim of this study is to explore the potential of EfficientNet with transfer learning for the classification of Glaucoma. The performance of the current work compares with other models, namely VGG16, InceptionV3, and Xception using public datasets such as RIM-ONEV2 & V3, ORIGA, DRISHTI-GS1, HRF, and ACRIMA. The dataset has split into training, validation, and testing with the ratio of 70:15:15. The assessment of the test dataset shows that the pre-trained EfficientNetB4 has achieved the highest performance value compared to other models listed above. The proposed method achieved 99.38% accuracy and also better results for other metrics, such as sensitivity, specificity, precision, F1_score, Kappa score, and Area Under Curve (AUC) compared to other models.

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APA Style
Geetha, A., Prakash, N.B. (2022). Classification of glaucoma in retinal images using efficientnetb4 deep learning model. Computer Systems Science and Engineering, 43(3), 1041-1055. https://doi.org/10.32604/csse.2022.023680
Vancouver Style
Geetha A, Prakash NB. Classification of glaucoma in retinal images using efficientnetb4 deep learning model. Comput Syst Sci Eng. 2022;43(3):1041-1055 https://doi.org/10.32604/csse.2022.023680
IEEE Style
A. Geetha and N.B. Prakash, "Classification of Glaucoma in Retinal Images Using EfficientnetB4 Deep Learning Model," Comput. Syst. Sci. Eng., vol. 43, no. 3, pp. 1041-1055. 2022. https://doi.org/10.32604/csse.2022.023680



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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