Texture Analysis-based Features Extraction & Classification of Lung Cancer Using Machine Learning

Author:

Swaroopa Korla1,Chaitanya Kumar N.2,Francis Britto Christopher3,Malathi M.4,Ganesan Karthika5,Kumar Sachin6

Affiliation:

1. Aditya Engineering College,Department of CSE,Surampalem- East Godavari,India,533437,

2. Sri Venkateswara Engineering College,Department of CSE,Tirupati,India,

3. Mahatma Gandhi University,Information Technology & Computer Services,Meghalaya,India,793101,

4. Vivekananda College of Engineering for Women (Autonomous),Department of ECE,Namakkal,India,637205,

5. Sri Vidya College of Engineering and Technology,Virudhunagar,India,626005,

6. Kyungpook National University,College of IT Engineering,Daegu,South Korea,

Abstract

Lung cancer is a form of carcinoma that develops as a result of aberrant cell growth or mutation in the lungs. Most of the time, this occurs due to daily exposure to hazardous chemicals. However, this is not the only cause of lung cancer; additional factors include smoking, indirect smoke exposure, family medical history, and so on. Cancer cells, unlike normal cells, proliferate inexorably and cluster together to create masses or tumors. The symptoms of this disease do not appear until cancer cells have moved to other parts of the body and are interfering with the healthy functioning of other organs. As a solution to this problem, Machine Learning (ML) algorithms are used to diagnose lung cancer. The image datasets for this study were obtained from Kaggle. The images are preprocessed using various approaches before being used to train the image model. Texture-based Feature Extraction (FE) algorithms such as Generalized Low-Rank Models (GLRM) and Gray-level co-occurrence matrix (GLCM) are then used to extract the essential characteristics from the image dataset. To develop a model, the collected features are given into ML classifiers like the Support Vector Machine (SVM) and the k-nearest neighbor's algorithm (k-NN).<br>

Publisher

BENTHAM SCIENCE PUBLISHERS

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