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A VGG16 feature-based transfer learning evaluation for the diagnosis of Oral Squamous Cell Carcinoma (OSCC)

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
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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