Open AccessDeep Learning Based Lung Cancer Detection and Classification
Author(s) -
N. Kalaivani,
Natraj Manimaran,
S. Sophia,
D. Devi
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/994/1/012026
Subject(s) - lung cancer , artificial intelligence , convolutional neural network , deep learning , boosting (machine learning) , computer science , artificial neural network , convolution (computer science) , pattern recognition (psychology) , lung , computed tomography , medicine , radiology , pathology
Lung diseases are indeed the lung-affecting diseases which impair the respiratory mechanism. Lung cancer has been one of the leading causes of mortality in humans worldwide. Early detection can enhance survival chances amid humans. If the condition is diagnosed in time, the average survival rates for people with lung cancer rise from 14 to 49 percent. While computed tomography (CT) is far more effective than X-ray, a thorough diagnosis includes multiple imaging approaches to support each other. A deep neural network for detecting lung cancer from CT images is developed and evaluated. For the classification of the lung image as normal or malignant, a densely connected convolution neural network (DenseNet) and adaptive boosting algorithm wasused. A dataset of 201 lung images is used in which 85% of the images are used for training and 15% of the images are used for testing and classification. Experimental results showed that the proposed method achieved an accuracy of 90.85%. Keywords: DenseNet, Image Processing, Deep Learning, Convolution Neural Networks (CNN).