The diagnostics of osteoarthritis : A fine-tuned transfer learning approach
Osteoarthritis (OA) is an illness that causes the wear of the protective cartilage between two bones in joints. Patients with OA disease suffer from pain in joints, stiffness, loss of flexibility, amongst others. Conventional means of identifying OA is considered laborious and prone to mistakes. Owi...
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Springer Science and Business Media Deutschland GmbH
2022
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/39468/1/The%20Diagnostics%20of%20Osteoarthritis_A%20Fine-Tuned%20Transfer%20Learning%20Approach.pdf http://umpir.ump.edu.my/id/eprint/39468/2/The%20diagnostics%20of%20osteoarthritis_A%20fine-tuned%20transfer%20learning%20approach_ABS.pdf http://umpir.ump.edu.my/id/eprint/39468/ https://doi.org/10.1007/978-3-030-97672-9_41 |
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| Summary: | Osteoarthritis (OA) is an illness that causes the wear of the protective cartilage between two bones in joints. Patients with OA disease suffer from pain in joints, stiffness, loss of flexibility, amongst others. Conventional means of identifying OA is considered laborious and prone to mistakes. Owing to the advancement of computer vision and computational models, automatic diagnostics is possible. Therefore, this paper proposes the use of transfer learning models for the classification of the different classes of OA. The pre-trained Convolutional Neural Network models used are VGG16, VGG19 and Resnet50, with their fully connected layers, are heuristically fine-tuned. It was demonstrated from this preliminary study that the fine-tuned VGG16 model could classify the classes fairly well in comparison to those that have been reported in the literature. |
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