LUNG CANCER DETECTION USING MACHINE LEARNING

Abstract

 The Main Objective of this research paper is to find out the early stage of lung 
cancer and explore the accuracy levels of various machine learning algorithms. After a 
systematic literature study, we found out that some classifiers have low accuracy and some 
are higher accuracy but difficult to reached nearer of 100%. Low accuracy and high 
implementation cost due to improper dealing with DICOM images. For medical image 
processing many different types of images are used but Computer Tomography (CT) scans 
are generally preferred because of less noise. Deep learning is proven to be the best method 
for medical image processing, lung nodule detection and classification, feature extraction and 
lung cancer stage prediction. In the first stage of this system used image processing 
techniques to extract lung regions. The segmentation is done using K Means. The features are 
extracted from the segmented images and the classification are done using various machine 
learning algorithm. The performances of the proposed approaches are evaluated based on 
their accuracy, sensitivity, specificity and classification time.