Deep learning-based CNN for multiclassification of ocular diseases using transfer learning
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...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21681163.2024.2335959 |
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| author | G Divya Deepak Subraya Krishna Bhat |
| author_facet | G Divya Deepak Subraya Krishna Bhat |
| author_sort | G Divya Deepak |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d61563c76c3f4c0a9386c25b5a099d25 |
| institution | Matheson Library |
| issn | 2168-1163 2168-1171 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| spelling | doaj-art-d61563c76c3f4c0a9386c25b5a099d252025-07-08T10:28:28ZengTaylor & Francis GroupComputer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization2168-11632168-11712024-12-0112110.1080/21681163.2024.2335959Deep learning-based CNN for multiclassification of ocular diseases using transfer learningG Divya Deepak0Subraya Krishna Bhat1Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaEffective 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.https://www.tandfonline.com/doi/10.1080/21681163.2024.2335959CNNcataractglaucomaeye diseasesocular diseases |
| spellingShingle | G Divya Deepak Subraya Krishna Bhat Deep learning-based CNN for multiclassification of ocular diseases using transfer learning Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization CNN cataract glaucoma eye diseases ocular diseases |
| title | Deep learning-based CNN for multiclassification of ocular diseases using transfer learning |
| title_full | Deep learning-based CNN for multiclassification of ocular diseases using transfer learning |
| title_fullStr | Deep learning-based CNN for multiclassification of ocular diseases using transfer learning |
| title_full_unstemmed | Deep learning-based CNN for multiclassification of ocular diseases using transfer learning |
| title_short | Deep learning-based CNN for multiclassification of ocular diseases using transfer learning |
| title_sort | deep learning based cnn for multiclassification of ocular diseases using transfer learning |
| topic | CNN cataract glaucoma eye diseases ocular diseases |
| url | https://www.tandfonline.com/doi/10.1080/21681163.2024.2335959 |
| work_keys_str_mv | AT gdivyadeepak deeplearningbasedcnnformulticlassificationofoculardiseasesusingtransferlearning AT subrayakrishnabhat deeplearningbasedcnnformulticlassificationofoculardiseasesusingtransferlearning |