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Home > Archives > Volume 20, No 8 (2022) > Article

DOI: 10.14704/nq.2022.20.8.NQ44728

A Novel Lightweight Deep Learning Hybrid CNN Model for Automatic Detection of Oral Squamous Cell Carcinoma (OSCC) Using Histopathological Images

Sayyada Hajera Begum, P Vidyullatha

Abstract

Oral squamous cell carcinoma (OSCC), is a type of cancer that causes the loss of the structural formation of layers and membranes in the oral cavity region. Withthe recent advent of Deep learning (DL) in biomedical image classification, the automated early diagnosis of oral histopathological images can aid in effective treatment of oral cancer. This work attempts to perform an automated classification of benign and malignant oral biopsy histopathological images by implementing a DLbased convolutional neural network (CNN) model for the initialanalysis of OSCC. For this research, we formulated a structure with additional layers of convolution and maxpooling and applied the transfer learning approach using four recently developed candidate pre-trained DL-CNN models namely NASNetLarge, InceptionNet, Xception, and DenseNet201. The proposed hybrid DenseNet model has recorded an accuracy of 91.25%.A comparison with other contemporary methods is also provided which showcases the effectiveness of the proposed model in the early detection of OSCC.

Keywords

Oral cancer detection, Oral squamous cell carcinoma (OSCC), Deep Learning (DL),Convolutional Neural Network (CNN).

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