An improved CNN-based architecture for automatic lung nodule classification.
Lung cancer is one of the most critical diseases due to its significant death rate compared to all other types of cancer. The early diagnosis of lung cancer that improves the patient's chance of surviving is mostly done in two phases: screening through CT scan imaging modality and, more importantly...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 60; no. 7; pp. 1977 - 1987 |
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| Autores principales: | , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2022
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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=157613924&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157613924 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jul2022 vid: 60 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 157613924 157613924 NLM35524089 157613924 10.1007/s11517-022-02578-0 NLM35524089 157613924 ppf: 1977 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An improved CNN-based architecture for automatic lung nodule classification. aug: au: Mahmood, Sozan Abdullah Ahmed, Hunar Abubakir affil: Computer Department, College of Science, University of Sulaimani, 46001, Sulaymaniyah, Kurdistan, Iraq sug: subj: Lung Neoplasms Diagnosis Lung Pathology Diagnosis, Computer Assisted Methods Lung Tomography, X-Ray Computed Methods Human ab: Lung cancer is one of the most critical diseases due to its significant death rate compared to all other types of cancer. The early diagnosis of lung cancer that improves the patient's chance of surviving is mostly done in two phases: screening through CT scan imaging modality and, more importantly the medical expert's reading of the scan, which is a time-consuming task and is vulnerable to errors. It is difficult to differentiate between malignant and benign nodules and biopsies are highly invasive, and patients with benign nodules may undergo unnecessary procedures. In this study, we propose a CNN-based computer-aided diagnosis system to automatically classify pulmonary nodules into benign or malignant. The proposed network architecture is based on AlexNet architecture that experiments with several types of layer ordering, hyperparameters, and functions for the various sides of the network. To build a well-trained model, several pre-processing steps are applied to the entire dataset, for instance segmentation, normalization, and zero centering. Finally, the proposed system obtained results with 98.7% accuracy, 98.6% sensitivity, and 98.9% specificity. The proposed model achieved superior performance compared to the AlexNet. The modifications in the original AlexNet is done to get a reasonable structure that has high nodule analysis sensitivity. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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