The classification of oral squamous cell carcinoma (OSCC) by means of transfer learning
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...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Springer Science and Business Media Deutschland GmbH
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/37556/1/The%20classification%20of%20oral%20squamous%20cell%20carcinoma%20%28OSCC%29%20by%20means%20of%20transfer%20learning.pdf http://umpir.ump.edu.my/id/eprint/37556/2/The%20classification%20of%20oral%20squamous%20cell%20carcinoma%20%28OSCC%29%20by%20means%20of%20transfer%20learning_ABS.pdf |
| _version_ | 1796995700022575104 |
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| author | Ahmad Ridhauddin, Abdul Rauf Wan Hasbullah, Mohd Isa Ismail, Mohd Khairuddin Mohd Azraai, Mohd Razman Mohd Hafiz, Arzmi Abdul Majeed, Anwar P. P. |
| author_facet | Ahmad Ridhauddin, Abdul Rauf Wan Hasbullah, Mohd Isa Ismail, Mohd Khairuddin Mohd Azraai, Mohd Razman Mohd Hafiz, Arzmi Abdul Majeed, Anwar P. P. |
| author_sort | Ahmad Ridhauddin, Abdul Rauf |
| collection | UMP |
| description | 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. |
| first_indexed | 2024-03-06T13:06:13Z |
| format | Conference or Workshop Item |
| id | UMPir37556 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T13:06:13Z |
| publishDate | 2022 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| spelling | UMPir375562023-08-28T00:59:54Z http://umpir.ump.edu.my/id/eprint/37556/ The classification of oral squamous cell carcinoma (OSCC) by means of transfer learning Ahmad Ridhauddin, Abdul Rauf Wan Hasbullah, Mohd Isa Ismail, Mohd Khairuddin Mohd Azraai, Mohd Razman Mohd Hafiz, Arzmi Abdul Majeed, Anwar P. P. T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures 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. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/37556/1/The%20classification%20of%20oral%20squamous%20cell%20carcinoma%20%28OSCC%29%20by%20means%20of%20transfer%20learning.pdf pdf en http://umpir.ump.edu.my/id/eprint/37556/2/The%20classification%20of%20oral%20squamous%20cell%20carcinoma%20%28OSCC%29%20by%20means%20of%20transfer%20learning_ABS.pdf 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 https://doi.org/10.1007/978-3-030-97672-9_34 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Ahmad Ridhauddin, Abdul Rauf Wan Hasbullah, Mohd Isa Ismail, Mohd Khairuddin Mohd Azraai, Mohd Razman Mohd Hafiz, Arzmi Abdul Majeed, Anwar P. P. The classification of oral squamous cell carcinoma (OSCC) by means of transfer learning |
| title | The classification of oral squamous cell carcinoma (OSCC) by means of transfer learning |
| title_full | The classification of oral squamous cell carcinoma (OSCC) by means of transfer learning |
| title_fullStr | The classification of oral squamous cell carcinoma (OSCC) by means of transfer learning |
| title_full_unstemmed | The classification of oral squamous cell carcinoma (OSCC) by means of transfer learning |
| title_short | The classification of oral squamous cell carcinoma (OSCC) by means of transfer learning |
| title_sort | classification of oral squamous cell carcinoma oscc by means of transfer learning |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/37556/1/The%20classification%20of%20oral%20squamous%20cell%20carcinoma%20%28OSCC%29%20by%20means%20of%20transfer%20learning.pdf http://umpir.ump.edu.my/id/eprint/37556/2/The%20classification%20of%20oral%20squamous%20cell%20carcinoma%20%28OSCC%29%20by%20means%20of%20transfer%20learning_ABS.pdf |
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