BID-Net: An Automated System for Bone Invasion Detection Occurring at Stage T4 in Oral Squamous Carcinoma Using Deep Learning.

Pinky Agarwal,Amitabha Chakrabarty,Vipin Pal,Anju Yadav,Pratistha Mathur

doi:10.1155/2022/4357088

Abstract

Detection of the presence and absence of bone invasion by the tumor in oral squamous cell carcinoma (OSCC) patients is very significant for their treatment planning and surgical resection. For bone invasion detection, CT scan imaging is the preferred choice of radiologists because of its high sensitivity and specificity. In the present work, deep learning algorithm based model, BID-Net, has been proposed for the automation of bone invasion detection. BID-Net performs the binary classification of CT scan images as the images with bone invasion and images without bone invasion. The proposed BID-Net model has achieved an outstanding accuracy of 93.62%. The model is also compared with six Transfer Learning models like VGG16, VGG19, ResNet-50, MobileNetV2, DenseNet-121, ResNet-101 and BID-Net outperformed over the other models. As there exists no previous studies on bone invasion detection using Deep Learning models, so the results of the proposed model have been validated from the experts of practitioner radiologists, S.M.S. hospital, Jaipur, India.

Highlights

Oral cancer is the sixth most dangerous cancer among all types of cancers worldwide [1]
DL techniques provide accurate and cost-effective solutions. e work of this paper presented a DL based framework, named BID-Net, that is a system for bone invasion detection in Oral Squamous carcinoma
Performance of the proposed BID-Net model is compared with six standard Transfer learning (TL) models. e simulation results confirmed that the proposed BID-Net has achieved outstanding accuracy and is cost-effective as well

Summary

Introduction

Oral cancer is the sixth most dangerous cancer among all types of cancers worldwide [1]. India reports the highest number of oral cancer cases and it accounts for one-third of the total number of cases globally. E increasing numbers of oral cancer cases are causing great concern among Indian health communities as they are discovered only after they have reached the advanced stages. In India, 70% of the oral cancer cases are detected in the advanced stage and due to this late detection survival rate is very less [3]. Erefore, these clinical examinations have to be supplemented with radiological imaging techniques to calculate accurate tumor size, depth of invasion and bone invasion(BI), etc. Various imaging techniques are used in oral cancer treatment. Suitable use of imaging techniques helps to understand staging of malignancy spread of the tumor to lymph nodes(LN) or distant organs and examination of vascularity. Imaging helps in the planning of resection, TNM staging, and their treatment

Methods

Results

Conclusion