Computer-Aided Diagnosis of Oral Squamous Cell Carcinoma: A Feature-Based Transfer Learning Approach

Anwar P. P. Abdul Majeed; Wan Hasbullah Mohd Isa; Ahmad Ridhauddin Abdul Rauf; Ahmad Fakhri Ahmad; Mohd Hafiz Arzmi; Hadyan Hafizh; Eng Hwa Yap

doi:10.1007/978-981-99-8498-5_34

Computer-Aided Diagnosis of Oral Squamous Cell Carcinoma: A Feature-Based Transfer Learning Approach

Anwar P. P. Abdul Majeed^*
, Wan Hasbullah Mohd Isa
, Ahmad Ridhauddin Abdul Rauf
, Ahmad Fakhri Ahmad
, Mohd Hafiz Arzmi
, Hadyan Hafizh
, Eng Hwa Yap

^*Corresponding author for this work

Universiti Malaysia Pahang Al-Sultan Abdullah
International Islamic University Malaysia
Xi'an Jiaotong-Liverpool University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

Oral cancer, particularly Oral Squamous Cell Carcinoma (OSCC), has a high mortality rate due to late detection. However, manual diagnosis is difficult and time-consuming. Hence, the employment of machine learning methods has been explored to aid diagnosis through automated image classification. This study aims to evaluate pipelines combining pre-trained VGG19 convolutional neural network (CNN) model that is used to extract discriminative features from normal and cancerous oral histopathology images. The extracted features were fed to different machine learning models, support vector machine (SVM), k-nearest neighbours (kNN), and random forest (RF) were trained to classify the images. It was demonstrated that the VGG199-RF yielded the best performance across the training, validation, and test dataset with a classification accuracy of 99%, 92%, and 90%, respectively, against other pipelines evaluated. The study demonstrates that feature-based transfer learning is an attractive and effective approach to be employed for computer-aided diagnosis.

Original language	English
Title of host publication	Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023
Editors	Andrew Tan, Fan Zhu, Haochuan Jiang, Kazi Mostafa, Eng Hwa Yap, Leo Chen, Lillian J. A. Olule, Hyun Myung
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	433-438
Number of pages	6
ISBN (Print)	9789819984978
DOIs	https://doi.org/10.1007/978-981-99-8498-5_34
Publication status	Published - 2024
Event	International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023 - Suzhou, China Duration: 22 Aug 2023 → 23 Aug 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	845
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023
Country/Territory	China
City	Suzhou
Period	22/08/23 → 23/08/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Computer-aided diagnosis
Deep learning
Feature-based transfer learning
Machine learning
Oral cancer
Oral squamous cell carcinoma

Access to Document

10.1007/978-981-99-8498-5_34

Cite this

P. P. Abdul Majeed, A., Mohd Isa, W. H., Abdul Rauf, A. R., Ahmad, A. F., Arzmi, M. H., Hafizh, H., & Yap, E. H. (2024). Computer-Aided Diagnosis of Oral Squamous Cell Carcinoma: A Feature-Based Transfer Learning Approach. In A. Tan, F. Zhu, H. Jiang, K. Mostafa, E. H. Yap, L. Chen, L. J. A. Olule, & H. Myung (Eds.), Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023 (pp. 433-438). (Lecture Notes in Networks and Systems; Vol. 845). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-8498-5_34

P. P. Abdul Majeed, Anwar ; Mohd Isa, Wan Hasbullah ; Abdul Rauf, Ahmad Ridhauddin et al. / Computer-Aided Diagnosis of Oral Squamous Cell Carcinoma : A Feature-Based Transfer Learning Approach. Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023. editor / Andrew Tan ; Fan Zhu ; Haochuan Jiang ; Kazi Mostafa ; Eng Hwa Yap ; Leo Chen ; Lillian J. A. Olule ; Hyun Myung. Springer Science and Business Media Deutschland GmbH, 2024. pp. 433-438 (Lecture Notes in Networks and Systems).

@inproceedings{395aa240aeb0475bae962d9d0f609956,

title = "Computer-Aided Diagnosis of Oral Squamous Cell Carcinoma: A Feature-Based Transfer Learning Approach",

abstract = "Oral cancer, particularly Oral Squamous Cell Carcinoma (OSCC), has a high mortality rate due to late detection. However, manual diagnosis is difficult and time-consuming. Hence, the employment of machine learning methods has been explored to aid diagnosis through automated image classification. This study aims to evaluate pipelines combining pre-trained VGG19 convolutional neural network (CNN) model that is used to extract discriminative features from normal and cancerous oral histopathology images. The extracted features were fed to different machine learning models, support vector machine (SVM), k-nearest neighbours (kNN), and random forest (RF) were trained to classify the images. It was demonstrated that the VGG199-RF yielded the best performance across the training, validation, and test dataset with a classification accuracy of 99\%, 92\%, and 90\%, respectively, against other pipelines evaluated. The study demonstrates that feature-based transfer learning is an attractive and effective approach to be employed for computer-aided diagnosis.",

keywords = "Computer-aided diagnosis, Deep learning, Feature-based transfer learning, Machine learning, Oral cancer, Oral squamous cell carcinoma",

author = "\{P. P. Abdul Majeed\}, Anwar and \{Mohd Isa\}, \{Wan Hasbullah\} and \{Abdul Rauf\}, \{Ahmad Ridhauddin\} and Ahmad, \{Ahmad Fakhri\} and Arzmi, \{Mohd Hafiz\} and Hadyan Hafizh and Yap, \{Eng Hwa\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023 ; Conference date: 22-08-2023 Through 23-08-2023",

year = "2024",

doi = "10.1007/978-981-99-8498-5\_34",

language = "English",

isbn = "9789819984978",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "433--438",

editor = "Andrew Tan and Fan Zhu and Haochuan Jiang and Kazi Mostafa and Yap, \{Eng Hwa\} and Leo Chen and Olule, \{Lillian J. A.\} and Hyun Myung",

booktitle = "Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023",

}

P. P. Abdul Majeed, A, Mohd Isa, WH, Abdul Rauf, AR, Ahmad, AF, Arzmi, MH, Hafizh, H & Yap, EH 2024, Computer-Aided Diagnosis of Oral Squamous Cell Carcinoma: A Feature-Based Transfer Learning Approach. in A Tan, F Zhu, H Jiang, K Mostafa, EH Yap, L Chen, LJA Olule & H Myung (eds), Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023. Lecture Notes in Networks and Systems, vol. 845, Springer Science and Business Media Deutschland GmbH, pp. 433-438, International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023, Suzhou, China, 22/08/23. https://doi.org/10.1007/978-981-99-8498-5_34

Computer-Aided Diagnosis of Oral Squamous Cell Carcinoma: A Feature-Based Transfer Learning Approach. / P. P. Abdul Majeed, Anwar; Mohd Isa, Wan Hasbullah; Abdul Rauf, Ahmad Ridhauddin et al.
Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023. ed. / Andrew Tan; Fan Zhu; Haochuan Jiang; Kazi Mostafa; Eng Hwa Yap; Leo Chen; Lillian J. A. Olule; Hyun Myung. Springer Science and Business Media Deutschland GmbH, 2024. p. 433-438 (Lecture Notes in Networks and Systems; Vol. 845).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Computer-Aided Diagnosis of Oral Squamous Cell Carcinoma

T2 - International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023

AU - P. P. Abdul Majeed, Anwar

AU - Mohd Isa, Wan Hasbullah

AU - Abdul Rauf, Ahmad Ridhauddin

AU - Ahmad, Ahmad Fakhri

AU - Arzmi, Mohd Hafiz

AU - Hafizh, Hadyan

AU - Yap, Eng Hwa

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

PY - 2024

Y1 - 2024

N2 - Oral cancer, particularly Oral Squamous Cell Carcinoma (OSCC), has a high mortality rate due to late detection. However, manual diagnosis is difficult and time-consuming. Hence, the employment of machine learning methods has been explored to aid diagnosis through automated image classification. This study aims to evaluate pipelines combining pre-trained VGG19 convolutional neural network (CNN) model that is used to extract discriminative features from normal and cancerous oral histopathology images. The extracted features were fed to different machine learning models, support vector machine (SVM), k-nearest neighbours (kNN), and random forest (RF) were trained to classify the images. It was demonstrated that the VGG199-RF yielded the best performance across the training, validation, and test dataset with a classification accuracy of 99%, 92%, and 90%, respectively, against other pipelines evaluated. The study demonstrates that feature-based transfer learning is an attractive and effective approach to be employed for computer-aided diagnosis.

AB - Oral cancer, particularly Oral Squamous Cell Carcinoma (OSCC), has a high mortality rate due to late detection. However, manual diagnosis is difficult and time-consuming. Hence, the employment of machine learning methods has been explored to aid diagnosis through automated image classification. This study aims to evaluate pipelines combining pre-trained VGG19 convolutional neural network (CNN) model that is used to extract discriminative features from normal and cancerous oral histopathology images. The extracted features were fed to different machine learning models, support vector machine (SVM), k-nearest neighbours (kNN), and random forest (RF) were trained to classify the images. It was demonstrated that the VGG199-RF yielded the best performance across the training, validation, and test dataset with a classification accuracy of 99%, 92%, and 90%, respectively, against other pipelines evaluated. The study demonstrates that feature-based transfer learning is an attractive and effective approach to be employed for computer-aided diagnosis.

KW - Computer-aided diagnosis

KW - Deep learning

KW - Feature-based transfer learning

KW - Machine learning

KW - Oral cancer

KW - Oral squamous cell carcinoma

UR - https://www.scopus.com/pages/publications/85187801208

U2 - 10.1007/978-981-99-8498-5_34

DO - 10.1007/978-981-99-8498-5_34

M3 - Conference Proceeding

AN - SCOPUS:85187801208

SN - 9789819984978

T3 - Lecture Notes in Networks and Systems

SP - 433

EP - 438

BT - Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023

A2 - Tan, Andrew

A2 - Zhu, Fan

A2 - Jiang, Haochuan

A2 - Mostafa, Kazi

A2 - Yap, Eng Hwa

A2 - Chen, Leo

A2 - Olule, Lillian J. A.

A2 - Myung, Hyun

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 22 August 2023 through 23 August 2023

ER -

P. P. Abdul Majeed A, Mohd Isa WH, Abdul Rauf AR, Ahmad AF, Arzmi MH, Hafizh H et al. Computer-Aided Diagnosis of Oral Squamous Cell Carcinoma: A Feature-Based Transfer Learning Approach. In Tan A, Zhu F, Jiang H, Mostafa K, Yap EH, Chen L, Olule LJA, Myung H, editors, Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023. Springer Science and Business Media Deutschland GmbH. 2024. p. 433-438. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-99-8498-5_34

Computer-Aided Diagnosis of Oral Squamous Cell Carcinoma: A Feature-Based Transfer Learning Approach

Abstract

Publication series

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UN SDGs

Keywords

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