Automated Detection of Oral Lesions based on Deep Learning for Early Detection of Oral Cancer

Automated Detection of Oral Lesions based on Deep  Learning for Early Detection of Oral Cancer

Project Guide: Prof. Rahul Kumar

Hitesh Mahajan, Siddhesh Khilari, Stephen Tupe, Sudarshan Kalbhor, Yash Lohar BE Students,

Department of Computer Engineering

Genba Sopanrao Moze College of Engineering, Balewadi, Pune

Abstract

This project addresses the pressing issue of oral cancer, a problem causing increasing death rates globally.The solution involves utilizing an Automated Detection system, a smart technology based on deep learning. Think of it as a digital detective trained to identify oral lesions, which are early indicators of oral cancer.These lesions can be detected through images and patient records. The project focuses on employing a powerful technique called Convolutional Neural Network (CNN) within deep learning to make this detection process efficient and accurate. The significance of this approach lies in its ability to identify these lesions at an early stage.By catching them early, doctors can avoid invasive and often uncomfortable diagnostic procedures.Instead, patientscan be diagnosed swiftly and non-invasively leading to improved outcomes and higher survival rates. The ultimate goal of the project is to create a reliable and fast system. This system not only aids in early detection but also empowers healthcare professionals with a tool that enhances their ability to spot potential oral cancer cases efficiently. By doing so, it contributes significantly to the overall fight against oral cancer, ultimately saving lives and improving the quality of patient care.

Keyword: patients, oral cancer, CNN technique