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Article

Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network

1
Department of Computer Application, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar 751030, India
2
Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar 751030, India
3
Department of Computer Science and Engineering, SRM University-AP, Guntur 522240, India
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2023, 20(3), 2131; https://doi.org/10.3390/ijerph20032131
Received: 8 December 2022 / Revised: 19 January 2023 / Accepted: 20 January 2023 / Published: 24 January 2023
(This article belongs to the Special Issue Machine Learning for Healthcare Applications)

Abstract

Worldwide, oral cancer is the sixth most common type of cancer. India is in 2nd position, with the highest number of oral cancer patients. To the population of oral cancer patients, India contributes to almost one-third of the total count. Among several types of oral cancer, the most common and dominant one is oral squamous cell carcinoma (OSCC). The major reason for oral cancer is tobacco consumption, excessive alcohol consumption, unhygienic mouth condition, betel quid eating, viral infection (namely human papillomavirus), etc. The early detection of oral cancer type OSCC, in its preliminary stage, gives more chances for better treatment and proper therapy. In this paper, author proposes a convolutional neural network model, for the automatic and early detection of OSCC, and for experimental purposes, histopathological oral cancer images are considered. The proposed model is compared and analyzed with state-of-the-art deep learning models like VGG16, VGG19, Alexnet, ResNet50, ResNet101, Mobile Net and Inception Net. The proposed model achieved a cross-validation accuracy of 97.82%, which indicates the suitability of the proposed approach for the automatic classification of oral cancer data.
Keywords: deep convolutional neural network; histopathological images; oral cancer deep convolutional neural network; histopathological images; oral cancer

Share and Cite

MDPI and ACS Style

Das, M.; Dash, R.; Mishra, S.K. Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network. Int. J. Environ. Res. Public Health 2023, 20, 2131. https://doi.org/10.3390/ijerph20032131

AMA Style

Das M, Dash R, Mishra SK. Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network. International Journal of Environmental Research and Public Health. 2023; 20(3):2131. https://doi.org/10.3390/ijerph20032131

Chicago/Turabian Style

Das, Madhusmita, Rasmita Dash, and Sambit Kumar Mishra. 2023. "Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network" International Journal of Environmental Research and Public Health 20, no. 3: 2131. https://doi.org/10.3390/ijerph20032131

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