An Innovative and Automatic Lung and Oral Cancer Classification Using Soft Computing Techniques

Lung and Oral Cancer are the most common diseases found in the majority of the populations in recent years. Medical diagnosis is colossally crucial but intricate task that should be accomplished exactly and proficiently. Although momentous progress has been made in the diagnosis and treatment of these diseases, further investigation is still desired. Soft Computing & DM techniques is the use of algorithms to mine the information and designs derived by the knowledge discovery from databases. Classification maps data into predefined groups or classes .The prognosis and diagnosis of cancer has been a challenging research problem for many researchers. The main objective of this proposed work is to compare the performance analysis of various soft computing and DM techniques to identify the Lung & Oral cancer prediction. This work employs different kinds of neural network classifiers. It confirms that the MLP networks produce more specific, accurate results compared to other techniques for Lung and Oral Cancer datasets.