Computer-Aided Diagnosis (CAD) of Pulmonary Nodule of Thoracic CT Image Using Transfer Learning.
Computer-aided diagnosis (CAD) has already been widely used in medical image processing. We recently make another trial to implement convolutional neural network (CNN) on the classification of pulmonary nodules of thoracic CT images. The biggest challenge in medical image classification with the hel...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 6; pp. 995 - 1008 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2019
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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=139568175&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139568175 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2019 vid: 32 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139568175 139568175 139568175 10.1007/s10278-019-00204-4 139568175 ppf: 995 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Computer-Aided Diagnosis (CAD) of Pulmonary Nodule of Thoracic CT Image Using Transfer Learning. aug: au: Zhang, Shikun Sun, Fengrong Wang, Naishun Zhang, Cuicui Yu, Qianlei Zhang, Mingqiang Babyn, Paul Zhong, Hai affil: School of Information Science and Engineering, Shandong University, Jinan, China sug: subj: Lung Neoplasms Classification Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Tomography, X-Ray Computed Learning Methods Human Validation Studies Descriptive Statistics ab: Computer-aided diagnosis (CAD) has already been widely used in medical image processing. We recently make another trial to implement convolutional neural network (CNN) on the classification of pulmonary nodules of thoracic CT images. The biggest challenge in medical image classification with the help of CNN is the difficulty of acquiring enough samples, and overfitting is a common problem when there are not enough images for training. Transfer learning has been verified as reasonable in dealing with such problems with an acceptable loss value. We use the classic LeNet-5 model to classify pulmonary nodules of thoracic CT images, including benign and malignant pulmonary nodules, and different malignancies of the malignant nodules. The CT images are obtained from Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) where both pulmonary nodule scanning and nodule annotations are available. These images are labeled and stored in a medical images knowledge base (KB), which is designed and implemented in our previous work. We implement the 10-folder cross validation (CV) to testify the robustness of the classification model we trained. The result demonstrates that the transfer learning of the LeNet-5 is good for classifying pulmonary nodules of thoracic CT images, and the average values of Top-1 accuracy are 97.041% and 96.685% respectively. We believe that our work is beneficial and has potential for practical diagnosis of lung nodules. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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