Automatic classification of solitary pulmonary nodules in PET/CT imaging employing transfer learning techniques.
Early and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computed Tomography (CT) chest scans can provide early treatment for patients with lung cancer, as well as doctor liberation from time-consuming procedures. The purpose of this study is the automatic and reliable characterization o...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 6; pp. 1299 - 1311 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2021
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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=150893739&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150893739 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2021 vid: 59 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 150893739 150377671 150893739 NLM34003394 10.1007/s11517-021-02378-y NLM34003394 150893739 ppf: 1299 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic classification of solitary pulmonary nodules in PET/CT imaging employing transfer learning techniques. aug: au: Apostolopoulos, Ioannis D. Pintelas, Emmanuel G. Livieris, Ioannis E. Apostolopoulos, Dimitris J. Papathanasiou, Nikolaos D. Pintelas, Panagiotis E. Panayiotakis, George S. affil: Department of Medical Physics, School of Medicine, University of Patras, 26504, Patras, Greece sug: subj: Solitary Pulmonary Nodule Lung Neoplasms Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted ab: Early and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computed Tomography (CT) chest scans can provide early treatment for patients with lung cancer, as well as doctor liberation from time-consuming procedures. The purpose of this study is the automatic and reliable characterization of SPNs in CT scans extracted from Positron Emission Tomography and Computer Tomography (PET/CT) system. To achieve the aforementioned task, Deep Learning with Convolutional Neural Networks (CNN) is applied. The strategy of training specific CNN architectures from scratch and the strategy of transfer learning, by utilizing state-of-the-art pre-trained CNNs, are compared and evaluated. To enhance the training sets, data augmentation is performed. The publicly available database of CT scans, named as Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), is also utilized to further expand the training set and is added to the PET/CT dataset. The results highlight the effectiveness of transfer learning and data augmentation for the classification task of small datasets. The best accuracy obtained on the PET/CT dataset reached 94%, utilizing a modification proposal of a state-of-the-art CNN, called VGG16, and enhancing the training set with LIDC-IDRI dataset. Besides, the proposed modification outperforms in terms of sensitivity several similar researches, which exploit the benefits of transfer learning. Overview of the experiment setup. The two datasets containing nodule representations are combined to evaluate the effectiveness of transfer learning over the traditional approach of training Convolutional Neural Networks from scratch. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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