Early Exposure of Lung Cancer by Combining ANN and SVM Algorithm
M. Sheik Mansoor1, M. Mohamed Sathik2
1M Sheik Mansoor, Research Scholar, Sadakathullah Appa College, Tirunelveli, affiliated to Manonmanium Sundaranar University, Tirunelveli, Tamilnadu, India.
2Dr. M Mohamed Sathik, Principal and Research Supervisor, Sadakathullah Appa College, Tirunelveli, affiliated to Manonmanium Sundaranar University, Tirunelveli, Tamilnadu, India.
Manuscript received on November 11, 2019. | Revised Manuscript received on November 20 2019. | Manuscript published on 30 November, 2019. | PP: 10675-10680 | Volume-8 Issue-4, November 2019. | Retrieval Number: D4278118419/2019©BEIESP | DOI: 10.35940/ijrte.D4278.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Lung cancer is a lethal type of cancers as its rate of spreading is very high compared to the other cancers. Patient who have been affected from Small Cell Lung Cancer (SCLC) has fast outspread rate. Even at initial stage, around 67-75% of cancer victims with SCLC will have fast outspreads and serious damages to the nearby physical parts. Moreover, World Health Organization (WHO) has predicted the count of lung cancer deaths will reach 9.6 million in 2020. Identifying such a lethal type of cancer early can be lifesaving one. Because, cancer cells in lungs are capable of traveling to other body parts even before the doctor detects them in lungs. In this research work, we have designed a combined approach to prognosticate lung cancer and its type using Artificial Neural Networks (ANN) and Support Vector Machine (SVM). To train both the ML algorithm, an open access patient health dataset published by cancer imaging archives is used. The dataset has the information like pretreatment CT scans, 3D image details of tumor and clinical outcomes. The results produced by ANN and SVM algorithm are compared to predict the type of the lung cancer accurately. The result holds good for a real time implementation.
Keywords: Neural Networks, Lung Cancer Prediction, Cancer diagnosis, Support Vector Machine.
Scope of the Article: Parallel and Distributed Algorithms.