LUNG CANCER CLASSIFICATION

Abstract

The main objective of this project is to assist clinicians to efficiently identify the Lung Cancer via forecasting analysis by using Morphological Neural Network (MNN) Classification with Image Pruning Methodology. In image processing domain, the complex task to analyze and rectify is cancer estimation and prediction. Compare to tumor estimations, Cancer estimations are more complex because these are purely cell based paradigms and visually not clear to analyze; especially this system focus on Lung cancer and its strategies. A new methodology is required to classify these kind of cancer with Image Pruning Scheme is introduced to efficiently classify the cancer cells and mark out the affected region more efficiently. Lung cancer is one of prevalent diseases in human, and can be diagnosed using several tests that include CT scans, MRI Scans, biopsy and so on. Over the years, the use of learning machine and artificial intelligence techniques has transformed the process of diagnosing lung Cancer. However, the accurate classification of cancer cells is still a medical challenge faced by researchers. Difficulties are routinely encountered in the search for sets of features that provide adequate distinctiveness required for classifying breast tissues into groups of normal and abnormal. Therefore, the aim of this approach is to prove that the MNN algorithm is more efficient for diagnosis, prognosis and prediction of lung abnormality, which is basically derived from two classical algorithms called Morphological Image Processing and Artificial Neural Network (ANN) with Image Pruning.