Evaluating Efficacy of Machine Learning in Oral cancer detection using Histopathological Image Database

In the modern world, with increasing cases of cancer diseases pose a huge problem to society. Conventional clinical method of cancer detection highly dependents on medical professionals, their devices and their clinical experience. With increasing cases of cancer, it's high time to find out different ways to train different models so that computer-aided systems can be built that can detect cancerous cells faster which can help in curing the disease faster and also help in decreasing the mortality rate. However, in the current era automation techniques implementing image analysis methods play a vital role for cancer detection. This study works on machine learning (ML) for automatic detection of oral cancer through histopathological oral cell dataset. To understand the images accurately, few preprocessing techniques such as Data augmentation, Color normalization, Image resizing, Pixel normalization, Edge detection and Wavelet transformation techniques are followed, which serves as the critical part of this research work. For classification purpose, specifically the machine learning classifiers such as Random Forest, KNN and SVM are considered and the superior one is evaluated.