Improving Oral Cancer Detection Using Pretrained Model

C. Kavyashree; H. S. Vimala; J. Shreyas

doi:10.1109/CICT56698.2022.9997897

Improving Oral Cancer Detection Using Pretrained Model

C. Kavyashree
, H. S. Vimala
, J. Shreyas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

Oral Cancer is a kind of cancer, if found early enough, has a great chance of survival. Artificial Intelligence techniques is helping in a great way by automating the cancer prediction at a faster pace and low cost. Deep learning algorithms are extremely helpful in automating the oral cancer detection. These algorithms are helpful in extracting the features and classifying the images. The Convolutional Neural Network is used in this paper to compare with pretrained DenseNet201, DenseNet169 and DenseNet121 model. The results of the DenseNet201 has shown a good improvement in detection accuracy with 85% and significantly reduces the loss whereas the DenseNet169 provides excellent training accuracy of 98.96%. DenseNet201 also performs well at recall and F1-score 93.93% and 93.22%. The pretrained model also improves computational efficiency by using predefined weights.

Original language	English
Title of host publication	2022 IEEE 6th Conference on Information and Communication Technology, CICT 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665473125
DOIs	https://doi.org/10.1109/CICT56698.2022.9997897
Publication status	Published - 2022
Event	6th IEEE Conference on Information and Communication Technology, CICT 2022 - Gwalior, India Duration: 18-11-2022 → 20-11-2022

Publication series

Name	2022 IEEE 6th Conference on Information and Communication Technology, CICT 2022

Conference

Conference	6th IEEE Conference on Information and Communication Technology, CICT 2022
Country/Territory	India
City	Gwalior
Period	18-11-22 → 20-11-22

UN SDGs

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

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Hardware and Architecture
Information Systems
Modelling and Simulation
Instrumentation

Access to Document

10.1109/CICT56698.2022.9997897

Cite this

Kavyashree, C., Vimala, H. S., & Shreyas, J. (2022). Improving Oral Cancer Detection Using Pretrained Model. In 2022 IEEE 6th Conference on Information and Communication Technology, CICT 2022 (2022 IEEE 6th Conference on Information and Communication Technology, CICT 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CICT56698.2022.9997897

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title = "Improving Oral Cancer Detection Using Pretrained Model",

abstract = "Oral Cancer is a kind of cancer, if found early enough, has a great chance of survival. Artificial Intelligence techniques is helping in a great way by automating the cancer prediction at a faster pace and low cost. Deep learning algorithms are extremely helpful in automating the oral cancer detection. These algorithms are helpful in extracting the features and classifying the images. The Convolutional Neural Network is used in this paper to compare with pretrained DenseNet201, DenseNet169 and DenseNet121 model. The results of the DenseNet201 has shown a good improvement in detection accuracy with 85\% and significantly reduces the loss whereas the DenseNet169 provides excellent training accuracy of 98.96\%. DenseNet201 also performs well at recall and F1-score 93.93\% and 93.22\%. The pretrained model also improves computational efficiency by using predefined weights.",

author = "C. Kavyashree and Vimala, \{H. S.\} and J. Shreyas",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 6th IEEE Conference on Information and Communication Technology, CICT 2022 ; Conference date: 18-11-2022 Through 20-11-2022",

year = "2022",

doi = "10.1109/CICT56698.2022.9997897",

language = "English",

series = "2022 IEEE 6th Conference on Information and Communication Technology, CICT 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2022 IEEE 6th Conference on Information and Communication Technology, CICT 2022",

address = "United States",

}

Kavyashree, C, Vimala, HS & Shreyas, J 2022, Improving Oral Cancer Detection Using Pretrained Model. in 2022 IEEE 6th Conference on Information and Communication Technology, CICT 2022. 2022 IEEE 6th Conference on Information and Communication Technology, CICT 2022, Institute of Electrical and Electronics Engineers Inc., 6th IEEE Conference on Information and Communication Technology, CICT 2022, Gwalior, India, 18-11-22. https://doi.org/10.1109/CICT56698.2022.9997897

Improving Oral Cancer Detection Using Pretrained Model. / Kavyashree, C.; Vimala, H. S.; Shreyas, J.
2022 IEEE 6th Conference on Information and Communication Technology, CICT 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE 6th Conference on Information and Communication Technology, CICT 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Improving Oral Cancer Detection Using Pretrained Model

AU - Kavyashree, C.

AU - Vimala, H. S.

AU - Shreyas, J.

PY - 2022

Y1 - 2022

N2 - Oral Cancer is a kind of cancer, if found early enough, has a great chance of survival. Artificial Intelligence techniques is helping in a great way by automating the cancer prediction at a faster pace and low cost. Deep learning algorithms are extremely helpful in automating the oral cancer detection. These algorithms are helpful in extracting the features and classifying the images. The Convolutional Neural Network is used in this paper to compare with pretrained DenseNet201, DenseNet169 and DenseNet121 model. The results of the DenseNet201 has shown a good improvement in detection accuracy with 85% and significantly reduces the loss whereas the DenseNet169 provides excellent training accuracy of 98.96%. DenseNet201 also performs well at recall and F1-score 93.93% and 93.22%. The pretrained model also improves computational efficiency by using predefined weights.

AB - Oral Cancer is a kind of cancer, if found early enough, has a great chance of survival. Artificial Intelligence techniques is helping in a great way by automating the cancer prediction at a faster pace and low cost. Deep learning algorithms are extremely helpful in automating the oral cancer detection. These algorithms are helpful in extracting the features and classifying the images. The Convolutional Neural Network is used in this paper to compare with pretrained DenseNet201, DenseNet169 and DenseNet121 model. The results of the DenseNet201 has shown a good improvement in detection accuracy with 85% and significantly reduces the loss whereas the DenseNet169 provides excellent training accuracy of 98.96%. DenseNet201 also performs well at recall and F1-score 93.93% and 93.22%. The pretrained model also improves computational efficiency by using predefined weights.

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U2 - 10.1109/CICT56698.2022.9997897

DO - 10.1109/CICT56698.2022.9997897

M3 - Conference contribution

AN - SCOPUS:85146678456

T3 - 2022 IEEE 6th Conference on Information and Communication Technology, CICT 2022

BT - 2022 IEEE 6th Conference on Information and Communication Technology, CICT 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th IEEE Conference on Information and Communication Technology, CICT 2022

Y2 - 18 November 2022 through 20 November 2022

ER -

Improving Oral Cancer Detection Using Pretrained Model

Abstract

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UN SDGs

All Science Journal Classification (ASJC) codes

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