A Hybrid Fuzzy-SVM classifier for automated lung diseases diagnosis

• Autorzy: Ben Hassen D. , Ben Zakour S. , Taleb H.

Identyfikatory

DOI

10.1515/pjmpe-2016-0017

• Języki publikacji: EN

Abstrakty

EN

A novel scheme for lesions classification in chest radiographs is presented in this paper. Features are extracted from detected lesions from lung regions which are segmented automatically. Then, we needed to eliminate redundant variables from the subset extracted because they affect the performance of the classification. We used Stepwise Forward Selection and Principal Components Analysis. Then, we obtained two subsets of features. We finally experimented the Stepwise/FCM/SVM classification and the PCA/FCM/SVM one. The ROC curves show that the hybrid PCA/FCM/SVM has relatively better accuracy and remarkable higher efficiency. Experimental results suggest that this approach may be helpful to radiologists for reading chest images.

Słowa kluczowe

EN

computer aided diagnosis; lung lesion classification; FCM; SVM; PCA

• Wydawca: Polskie Towarzystwo Fizyki Medycznej
• Czasopismo: Polish Journal of Medical Physics and Engineering
• Rocznik: 2016
• Tom: Vol. 22, Iss. 4
• Strony: 97--103
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Ben Hassen D.

• Faculty of law, economics and management of Jendouba, University of Jendouba, Tunisia

autor

Ben Zakour S.

• Faculty of law, economics and management of Jendouba, University of Jendouba, Tunisia

autor

Taleb H.

• Faculty of economics and management of Nebeul, University of Carthage, Tunisia

Bibliografia

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