The classification of oral squamous cell carcinoma (OSCC) by means of transfer learning

Ahmad Ridhauddin, Abdul Rauf and Wan Hasbullah, Mohd Isa and Ismail, Mohd Khairuddin and Mohd Azraai, Mohd Razman and Mohd Hafiz, Arzmi and Abdul Majeed, Anwar P. P. (2022) The classification of oral squamous cell carcinoma (OSCC) by means of transfer learning. In: Lecture Notes in Networks and Systems; 9th International Conference on Robot Intelligence Technology and Applications, RiTA 2021 , 16-17 December 2021 , Daejeon. pp. 386-391., 429. ISBN 978-303097671-2

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Abstract

Patients that are diagnosed with oral cancer has more than an 83% survival chance if it is detected in its early stages. However, through conventional labour-intensive means, only 29% of cases are detected. It is worth mentioning that 90% of oral cancer is Oral Squamous Cell Carcinoma (OSCC) and is often caused by smoking and alcohol consumption. Computer-aided diagnostics could further increase the rate of detection of this form of oral cancer. The present study sought to employ a class of deep learning techniques known as transfer learning. The Inception V3 pre-trained convolutional neural network model is used to extract the features from texture-based images. Consequently, the malignant and benign nature of the cancer is identified from three different machine learning models, i.e., Support Vector Machine (SVM), k-Nearest Neighbors (kNN) and Random Forest (RF). It was shown from the study that an average of 91% classification accuracy was obtained from the test and validation dataset from the Inception V3-RF pipeline. The outcome of the present study could serve useful in an objective-based automatic diagnostic of OSCC and hence could possibly increase its detection.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	InceptionV3; kNN; Oral cancer; Oral squamous cell carcinoma; RF; SVM; Transfer learning
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TS Manufactures
Faculty/Division:	Institute of Postgraduate Studies Faculty of Manufacturing and Mechatronic Engineering Technology
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	28 Aug 2023 00:59
Last Modified:	28 Aug 2023 00:59
URI:	http://umpir.ump.edu.my/id/eprint/37556
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