Deep Learning Based Lung Cancer Detection and Classification
N. Kalaivani, Natraj Manimaran, S. Sophia, S. Devi
Abstract
Abstract 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).