Automated Histopathologic Oral Cancer Detection using Deep Learning for Early Diagnosis

Abstract

An Innovative way to solve the very important problem in the world of early diagnosis is to use deep learning to automate the histopathologic discovery of oral cancer. The death rate goes up a lot when oral cancer is found late, which shows how important it is to have quick, accurate, and scalable testing tools. This study aims to use advanced deep-learning techniques on histopathological images to find oral cancer more quickly and accurately. It researched several convolutional neural network (CNN) designs and shows that the EfficientNetB3 with attention mechanism technique model performed better. This model got the accurately tells the difference between cancerous and noncancerous cells with an impressive 95% accuracy. The suggested method reduces the death rate. Our research will help in timely detection of diseases and reduce mortality. There are issues with the method, such as the rising amount of histopathology data and the small number of trained pathologists available. However, it also makes using AI to diagnose health problems possible. This adaptable and low-cost choice could change how diagnoses are made, especially in places that lack of resources and where it's hard to get specialized care. Our study results show that EfficientNet-B3 with attention mechanism technique might help find oral cancer early, make the treatment work better, and lower the death rate. We care about people with oral cancer, as shown by our plan to make diagnosis faster and more accurate and lower the number of people who get it around the world. More research is needed into how the model can be used in real healthcare systems and how it can be better for a wider range of clinical cases. This will help patients get better care and save many life’s.