Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks.
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....
| Publicado en: | BioMed Research International Vol. 2017; pp. 1 - 7 |
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| Autores principales: | , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/13/2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=124587750&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124587750 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/13/2017 vid: 2017 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 124587750 124587750 124587750 10.1155/2017/4067832 124587750 ppf: 1 ppct: 6 formats: fmt: @attributes: type: P tig: atl: Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks. aug: au: Teramoto, Atsushi Tsukamoto, Tetsuya Kiriyama, Yuka Fujita, Hiroshi affil: School of Health Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake City, Aichi 470-1192, Japan sug: subj: Automation Lung Neoplasms Classification Cytological Techniques Neural Networks (Computer) Human Diagnosis, Differential Lung Neoplasms Mortality Lung Neoplasms Diagnosis Adenocarcinoma Carcinoma, Squamous Cell Carcinoma, Small Cell Diagnosis Microscopy Diagnostic Imaging Databases Graphics Data Collection Funding Source ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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