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.
