Arzmi, Mohd Hafiz and P.P. Abdul Majeed, Anwar and Musa, Rabiu Muazu and Mohd Razman, Mohd Azraai and Gan, Hong-Seng and Mohd Khairuddin, Ismail and Ab. Nasir, Ahmad Fakhri (2023) A VGG16 feature-based transfer learning evaluation for the diagnosis of Oral Squamous Cell Carcinoma (OSCC). In: Deep Learning in Cancer Diagnostics. Springer, Singapore, pp. 9-13. ISBN 978-981-19-8936-0
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Abstract
Oral Squamous Cell Carcinoma (OSCC) is the most prevalent type of oral cancer. Early detection of such cancer could increase a patient’s survival rate by 83%. This chapter shall explore the use of a feature-based transfer learning model, i.e., VGG16 coupled with different types of conventional machine learning models, viz. Support Vector Machine (SVM), Random Forest as well as k-Nearest Neighbour (kNN) as a means to identify OSCC. A total of 990 evenly distributed normal and OSCC histopathological images are split into the 60:20:20 ratio for training, testing and validation, respectively. A testing accuracy of 93% was recorded via the VGG16- RF pipeline from the study. Consequently, the proposed architecture is suitable to be deployed as artificial intelligence-driven computer-aided diagnostics and, in turn, facilitate clinicians for the identification of OSCC.
| Item Type: | Book Chapter |
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| Uncontrolled Keywords: | Computer-Aided Diagnosis; Transfer Learning; Oral Cancer; OSCC |
| Subjects: | R Medicine > R Medicine (General) R Medicine > RK Dentistry |
| Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): | Kulliyyah of Dentistry > Department of Fundamental Dental and Medical Sciences Kulliyyah of Dentistry |
| Depositing User: | AP Ts Dr Mohd Hafiz Arzmi |
| Date Deposited: | 09 Mar 2023 13:45 |
| Last Modified: | 09 May 2024 11:41 |
| URI: | http://irep.iium.edu.my/id/eprint/103896 |
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