Deep learning-based CNN for multiclassification of ocular diseases using transfer learning
Document Type
Article
Publication Title
Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
Abstract
Effective and timely diagnosis and treatment of ocular diseases is essential for swift recovery of the patients. Among ocular diseases, cataract and glaucoma are the most prevalent globally and need adequate attention. The present paper aims to develop an optimised deep learning based convolutional neural network (CNN) for the multi-classification of ocular diseases (normal, glaucoma and cataract). Three pre-trained CNNs (SqueezeNet, Darknet-53, EfficientNet-b0) were optimised concerning batch size (6/8/10) & optimiser type (SGDM, RMSProp, Adam) for obtaining maximum possible accuracy in the detection of multiple ocular diseases (cataract & glaucoma). Darknet-53 (batch size-6, optimiser type-Adam) gave the highest accuracy of 99.4% for a test sample of 1000 images. The performance metrics of Darknet-53 have been computed using a confusion matrix. Confusion matrix is also applied to calculate accuracy, sensitivity, specificity, f1 score and receiver operating curve (ROC). Through comparative performance analysis of the three CNNs, SqueezeNet, Darknet-53 and EfficientNet-b0 achieved the highest accuracy of 95%, 99.4% and 90%, respectively. The results indicate the importance of batch size and optimiser type on the performance of CNN models.
DOI
10.1080/21681163.2024.2335959
Publication Date
1-1-2024
Recommended Citation
Deepak, G. Divya and Bhat, Subraya Krishna, "Deep learning-based CNN for multiclassification of ocular diseases using transfer learning" (2024). Open Access archive. 7108.
https://impressions.manipal.edu/open-access-archive/7108
