Journal
NEURAL NETWORKS
Volume 128, Issue -, Pages 47-60Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2020.05.003
Keywords
Oral squamous cell carcinoma; Deep learning; Convolution neural network; Transfer learning; Biopsy
Funding
- Institute of Advanced Study in Science and Technology, Assam, an autonomous R&D institute under the Department of Science and Technology
- New Delhi, India
Ask authors/readers for more resources
The analysis of tissue of a tumor in the oral cavity is essential for the pathologist to ascertain its grading. Recent studies using biopsy images reveal computer-aided diagnosis for oral sub-mucous fibrosis (OSF) carried out using machine learning algorithms, but no research has yet been outlined for multi-class grading of oral squamous cell carcinoma (OSCC). Pertinently, with the advent of deep learning in digital imaging and computational aid in the diagnosis, multi-class classification of OSCC biopsy images can help in timely and effective prognosis and multi-modal treatment protocols for oral cancer patients, thus reducing the operational workload of pathologists while enhancing management of the disease. With this motivation, this study attempts to classify OSCC into its four classes as per the Broder's system of histological grading. The study is conducted on oral biopsy images applying two methods: (i) through the application of transfer learning using pre-trained deep convolutional neural network (CNN) wherein four candidate pre-trained models, namely Alexnet, VGG-16, VGG-19 and Resnet-50, were chosen to find the most suitable model for our classification problem, and (ii) by a proposed CNN model. Although the highest classification accuracy of 92.15% is achieved by Resnet-50 model, the experimental findings highlight that the proposed CNN model outperformed the transfer learning approaches displaying accuracy of 97.5%. It can be concluded that the proposed CNN based multi-class grading method of OSCC could be used for diagnosis of patients with OSCC. (C) 2020 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available