Deep Learning Based Lung Cancer Detection and Classification

Autor:	D. Devi, S. Sophia, N Kalaivani, N Manimaran
Rok vydání:	2020
Předmět:	medicine.medical_specialty Lung Artificial neural network business.industry Deep learning Early detection Image processing medicine.disease Convolutional neural network Convolution medicine.anatomical_structure medicine Artificial intelligence Radiology Lung cancer business
Zdroj:	IOP Conference Series: Materials Science and Engineering. 994:012026
ISSN:	1757-899X 1757-8981
DOI:	10.1088/1757-899x/994/1/012026
Popis:	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).
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d95f2dc8c560b7fb2eba06f1963a2d6e https://doi.org/10.1088/1757-899x/994/1/012026 Zobrazit plný text záznamu