A Deep CNN Framework for Oral Cancer Detection Using Histopathology Dataset

Abstract

One of the most common oncological types is oral cancer. Although medical technology has advanced at a phenomenal rate, high fatality has been observed in developing countries due to the lack of early stage diagnosis of the disease. The most fundamental symptom being the prolonged inflammation in the mouth areas. Cancers in the tongue, lips, cheeks, the floor of the mouth, hard and soft palates, sinuses, and pharynx (throat) are all considered oral cancers. This study focuses on early stage diagnosis of the disease using deep learning frameworks. It will offer a more thorough understanding of the disease and help experts make judgments about diagnostic and treatment options that are well-versed. We have used a deep learning framework based on the modified Convolutional Neural Network (CNN) that uses different sizes of hidden layers. The dataset comprised histopathology images. Histopathology datasets have the potential to transform the field of medical research. By feeding a histopathology dataset into a deep-learning framework, researchers can rapidly and precisely classify patterns in the data that would otherwise be difficult or impossible to detect. It could lead to faster diagnosis of diseases and more effective treatments. A total of 8000 images (4000 for each category of the cancers) are used for result analysis. Per epoch, the testing loss likewise diminishes gradually. As a final result, at 30 epochs, it has reached the highest accuracy of 97.6%. The convolutional neural network exhibits result which fare better than peer proposals in literature.