TY - GEN
T1 - The Classification of Oral Squamous Cell Carcinoma (OSCC) by Means of Transfer Learning
AU - Abdul Rauf, Ahmad Ridhauddin
AU - Mohd Isa, Wan Hasbullah
AU - Khairuddin, Ismail Mohd
AU - Mohd Razman, Mohd Azraai
AU - Arzmi, Mohd Hafiz
AU - P. P. Abdul Majeed, Anwar
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - InceptionV3
KW - Oral cancer
KW - Oral squamous cell carcinoma
KW - RF
KW - SVM
KW - Transfer learning
KW - kNN
UR - https://www.scopus.com/pages/publications/85128454665
U2 - 10.1007/978-3-030-97672-9_34
DO - 10.1007/978-3-030-97672-9_34
M3 - Conference Proceeding
SN - 9783030976712
VL - 429 LNNS
T3 - Lecture Notes in Networks and Systems
SP - 386
EP - 391
BT - Robot Intelligence Technology and Applications 6 - Results from the 9th International Conference on Robot Intelligence Technology and Applications
A2 - Kim, Jinwhan
A2 - Englot, Brendan
A2 - Park, Hae-Won
A2 - Choi, Han-Lim
A2 - Myung, Hyun
A2 - Kim, Junmo
A2 - Kim, Jong-Hwan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Conference on Robot Intelligence Technology and Applications, RiTA 2021
Y2 - 16 December 2021 through 17 December 2021
ER -