Computer-Assisted Image Processing System for Early Assessment of Lung Nodule Malignancy.
Simple Summary: Lung cancer is the second most common cancer in men after prostate cancer and in women after breast cancer, but it is the leading cause of cancer death among both genders. This manuscript proposes a new computer-aided diagnosis system that uses only a single computed tomography scan...
| Publicado en: | Cancers Vol. 14; no. 5; pp. 1117 - 1118 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
MDPI
Mar2022
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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=155707315&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155707315 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Mar2022 vid: 14 iid: 5 pid: 97109 pub: MDPI artinfo: ui: 155707315 155707315 155707315 10.3390/cancers14051117 155707315 ppf: 1117 ppct: 1 formats: tig: atl: Computer-Assisted Image Processing System for Early Assessment of Lung Nodule Malignancy. aug: au: Shaffie, Ahmed Soliman, Ahmed Eledkawy, Amr van Berkel, Victor El-Baz, Ayman affil: BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA sug: subj: Lung Pathology Solitary Pulmonary Nodule Diagnosis Early Detection of Cancer Methods Lung Radiography Image Processing, Computer Assisted Methods Tomography, X-Ray Computed Methods Human Image Processing, Computer Assisted Equipment and Supplies Tomography, X-Ray Computed Equipment and Supplies Sensitivity and Specificity Autoanalyzers Coding, Computer-Assisted ab: Simple Summary: Lung cancer is the second most common cancer in men after prostate cancer and in women after breast cancer, but it is the leading cause of cancer death among both genders. This manuscript proposes a new computer-aided diagnosis system that uses only a single computed tomography scan to diagnose the pulmonary nodule as benign or malignant. This system helps in the early detection of the pulmonary nodules and shows its ability to identify the pulmonary nodules precisely. Lung cancer is one of the most dreadful cancers, and its detection in the early stage is very important and challenging. This manuscript proposes a new computer-aided diagnosis system for lung cancer diagnosis from chest computed tomography scans. The proposed system extracts two different kinds of features, namely, appearance features and shape features. For the appearance features, a Histogram of oriented gradients, a Multi-view analytical Local Binary Pattern, and a Markov Gibbs Random Field are developed to give a good description of the lung nodule texture, which is one of the main distinguishing characteristics between benign and malignant nodules. For the shape features, Multi-view Peripheral Sum Curvature Scale Space, Spherical Harmonics Expansion, and a group of some fundamental morphological features are implemented to describe the outer contour complexity of the nodules, which is main factor in lung nodule diagnosis. Each feature is fed into a stacked auto-encoder followed by a soft-max classifier to generate the initial malignancy probability. Finally, all these probabilities are combined together and fed to the last network to give the final diagnosis. The system is validated using 727 nodules which are subset from the Lung Image Database Consortium (LIDC) dataset. The system shows very high performance measures and achieves 92.55 % , 91.70 % , and 93.40 % for the accuracy, sensitivity, and specificity, respectively. This high performance shows the ability of the system to distinguish between the malignant and benign nodules precisely. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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