This paper is published in Volume-5, Issue-4, 2019
Area
Medical Image Analysis
Author
Endalew Simie
Co-authors
Mandeep Kaur
Org/Univ
Sharda University, Greater Noida, Uttar Pradesh, India
Pub. Date
27 July, 2019
Paper ID
V5I4-1251
Publisher
Keywords
Lung cancer, Cancer classification, Nodule detection, 3D CNN, Deep learning

Citationsacebook

IEEE
Endalew Simie, Mandeep Kaur. Lung cancer detection using Convolutional Neural Network (CNN), International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Endalew Simie, Mandeep Kaur (2019). Lung cancer detection using Convolutional Neural Network (CNN). International Journal of Advance Research, Ideas and Innovations in Technology, 5(4) www.IJARIIT.com.

MLA
Endalew Simie, Mandeep Kaur. "Lung cancer detection using Convolutional Neural Network (CNN)." International Journal of Advance Research, Ideas and Innovations in Technology 5.4 (2019). www.IJARIIT.com.

Abstract

Lung cancer is a dangerous disease that taking human life rapidly worldwide. The death of the people is increasing exponentially because of lung cancer. In order to reduce the disease and save a human's life, the automated system is needed. The purpose of the lung cancer detection system is able to detect and provide reliable information to doctors and clinicians from the medical image. To minimize this problem, many systems have been proposed by using different image processing techniques, machine learning, and deep learning techniques. A computed tomography (CT) imaging modality is an efficient technique for medical screening used for lung cancer detection and diagnosis. Physician and radiologist use the CT scan images to analyze, interpret and diagnose the lung cancer from lung tissues. However, in most cases, obtaining an accurate diagnosis result without using the extra medical tool known as a computer- Aid detection and Diagnosis (CAD) system is tedious work for many physicians. To obtain an accurate result from computer-aided diagnosis system lung segmentation methods are basic once. So in this project, we have used different lung segmentation and nodules segmentation methods. Our work has consisted of preprocessing, and lung segmentation by using thresholding, and also used the U-net model for detection of the candidate nodules of the patient’s lung CT scan and classification methodology. We are used a convolutional neural network and designed a 3D CNN model that has 0.77% accuracy performance.
Paper PDF