LUNG CANCER DETECTING USING MACHINE LEARNING
Jayesh Koli
computer engineering department Sandip institute of technology and research centre Nashik, India jayeshmkoli13@gmail.com
Atharv Borse
Computer engineer department Sandip institute of technology and research centre Nashik, India atharvaborse30@gmail.com
Prof. Pramod.G. Patil
Computer engineering department Sandip institute of technology and research centre Nashik, India pgpatil11@sitrc.og
Pranav Wagh
Computer engineer department Sandip institute of technology and research centre Nashik, India pranavwagh7666@gmail.com
Krutika karad
Computer engineering department Sandip institute of technology and research centre Nashik, India krutikarad1821@gmail.com
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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 arehigher 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.
Key Words: Structural Co-occurrence Matrix (SCM), Classifier, Data Set, ROC curve, Malignant nodule, Benign nodule.
