Deep Lung Cancer Prediction and Segmentation on CT Scan
Anuja J1, Smitha Vas P2
1Anuja J, M.Tech. Scholar, Department of Computer Science and Engineering, LBS Institute of Technology for Women, Thiruvananthapuram (Kerala), India.
2Smitha Vas P, Assistant Professor, Department of Computer Science and Engineering, LBS Institute of Technology for Women, Thiruvananthapuram (Kerala), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 2308-2313 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7753068519/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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 cancers are one of the world’s lethal ailments and early prognosis of cancer is a complex mission in the detection of lung cancer. Analysis and treatment of lung malignancy has been one of the greatest problem faced by humans in the last few years. Early identification of the tumour would consistently make it easier to save a large number of lives across the globe. This paper presents an approach to classify tumour found in the lung as malignant or benign using a Convolutional Neural Network. Here, an Inception V3 model is used to predict if the lung is malignant or benign. The accuracy obtained through CNN is 97 percent, which is more efficient than traditional neural network system.
Keywords: Chest CT image, Computed Tomography, Convolutional Neural Network, Deep Learning, Lung cancer.
Scope of the Article: Deep Learning