Analysis of Oral Cancer Detection based Segmentation and Classification using Deep Learning Algorithms

DOI

10.2991/978-94-6463-471-6_66How to use a DOI?

Keywords

Artificial Intelligence; Convolutional Neural Network; Classification; Deep Learning; Oral Cancer and Segmentation

Abstract

Oral cancer is deadly cancer which is majorly spread in less and middle-income countries. The early diagnosis of oral cancer may attained through automatic detection of cancerous and malignant mouth lesions. Various researches developed a Machine Learning (ML) method which detects oral cancer from images. Though, there still lack in huge precision in the detection of oral cancer. Recently, development of Artificial Intelligence (AI), Deep Learning (DL) algorithms effectively detects the oral cancer in early and maximizes a patient’s survival rate. This survey analysis different DL algorithms such as Modified K-Means and Fuzzy C-means (modified KFCM), UNet depended Bayesian Deep Learning (BDL), Capsule network and CariesNet which was used for segmentation of oral cancer. Then, AlexNet, Enhanced Grasshopper Optimization Algorithm (EGOA) depended Deep Belief Network (DBN), Convolutional Neural Network (CNN) and Deep CNN was used for classification of oral cancer. The performance metrics used for evaluating the algorithms are Dice coefficient, Jaccard, Accuracy, Mean IoU, Precision, Weighted IoU, Specificity, Recall, Sensitivity, Error rate, F1-score and AUC.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Pullaiah Pinnika
AU  - K. Venkata Rao
PY  - 2024
DA  - 2024/07/30
TI  - Analysis of Oral Cancer Detection based Segmentation and Classification using Deep Learning Algorithms
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 683
EP  - 690
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_66
DO  - 10.2991/978-94-6463-471-6_66
ID  - Pinnika2024
ER  -