Feature Selection for Automatic Tuberculosis Screening in Frontal Chest Radiographs.
To detect pulmonary abnormalities such as Tuberculosis (TB), an automatic analysis and classification of chest radiographs can be used as a reliable alternative to more sophisticated and technologically demanding methods (e.g. culture or sputum smear analysis). In target areas like Kenya TB is highl...
| Publicado en: | Journal of Medical Systems Vol. 42; no. 8; pp. 1 - 2 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images equations & formulas review Journal Article |
| Publicado: |
Springer Nature
Aug2018
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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=131094268&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131094268 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Aug2018 vid: 42 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 131094268 131094268 131094268 10.1007/s10916-018-0991-9 131094268 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Feature Selection for Automatic Tuberculosis Screening in Frontal Chest Radiographs. aug: au: Vajda, Szilárd Karargyris, Alexandros Jaeger, Stefan Santosh, K.C. Candemir, Sema Xue, Zhiyun Antani, Sameer Thoma, George affil: Central Washington University, Ellensburg, WA, USA sug: subj: Health Screening Automation Tuberculosis Diagnosis Radiography, Thoracic Quality Improvement Radiography, Thoracic Classification Cell Culture Techniques Sputum Analysis Diagnostic Imaging Health Information Networks Neural Networks (Computer) Lung Anatomy and Histology Data Analysis Protocols ROC Curve ab: To detect pulmonary abnormalities such as Tuberculosis (TB), an automatic analysis and classification of chest radiographs can be used as a reliable alternative to more sophisticated and technologically demanding methods (e.g. culture or sputum smear analysis). In target areas like Kenya TB is highly prevalent and often co-occurring with HIV combined with low resources and limited medical assistance. In these regions an automatic screening system can provide a cost-effective solution for a large rural population. Our completely automatic TB screening system is processing the incoming CXRs (chest X-ray) by applying image preprocessing techniques to enhance the image quality followed by an adaptive segmentation based on model selection. The delineated lung regions are described by a multitude of image features. These characteristics are than optimized by a feature selection strategy to provide the best description for the classifier, which will later decide if the analyzed image is normal or abnormal. Our goal is to find the optimal feature set from a larger pool of generic image features, -used originally for problems such as object detection, image retrieval, etc. For performance evaluation measures such as under the curve (AUC) and accuracy (ACC) were considered. Using a neural network classifier on two publicly available data collections, -namely the Montgomery and the Shenzhen dataset, we achieved the maximum area under the curve and accuracy of 0.99 and 97.03%, respectively. Further, we compared our results with existing state-of-the-art systems and to radiologists’ decision. pubtype: Academic Journal doctype: diagnostic images equations & formulas review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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