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

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.

journal_name

Biomed Res Int

journal_title

BioMed research international

authors

Teramoto A,Tsukamoto T,Kiriyama Y,Fujita H

doi

10.1155/2017/4067832

subject

Has Abstract

pub_date

2017-01-01 00:00:00

pages

4067832

eissn

2314-6133

issn

2314-6141

journal_volume

2017

pub_type

杂志文章

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