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BioMed Research International
Volume 2017, Article ID 4067832, 6 pages
https://doi.org/10.1155/2017/4067832
Research Article

Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks

1School of Health Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake City, Aichi 470-1192, Japan
2School of Medicine, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake City, Aichi 470-1192, Japan
3Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan

Correspondence should be addressed to Atsushi Teramoto; pj.ca.uh-atijuf@otomaret

Received 5 May 2017; Revised 20 June 2017; Accepted 5 July 2017; Published 13 August 2017

Academic Editor: Noriyoshi Sawabata

Copyright © 2017 Atsushi Teramoto et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Lung cancer is a leading cause of death worldwide. Currently, in differential diagnosis of lung cancer, accurate classification of cancer types (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma) is required. However, improving the accuracy and stability of diagnosis is challenging. In this study, we developed an automated classification scheme for lung cancers presented in microscopic images using a deep convolutional neural network (DCNN), which is a major deep learning technique. The DCNN used for classification consists of three convolutional layers, three pooling layers, and two fully connected layers. In evaluation experiments conducted, the DCNN was trained using our original database with a graphics processing unit. Microscopic images were first cropped and resampled to obtain images with resolution of 256 × 256 pixels and, to prevent overfitting, collected images were augmented via rotation, flipping, and filtering. The probabilities of three types of cancers were estimated using the developed scheme and its classification accuracy was evaluated using threefold cross validation. In the results obtained, approximately 71% of the images were classified correctly, which is on par with the accuracy of cytotechnologists and pathologists. Thus, the developed scheme is useful for classification of lung cancers from microscopic images.