title: An Improved CAD System for Breast Cancer Diagnosis Based on Generalized Pseudo-Zernike Moment and Ada-DEWNN Classifier.
abstract: In this paper, a novel framework of computer-aided diagnosis (CAD) system has been presented for the classification of benign/malignant breast tissues. The properties of the generalized pseudo-Zernike moments (GPZM) and pseudo-Zernike moments (PZM) are utilized as suitable texture descriptors of the suspicious region in the mammogram. An improved classifier- adaptive differential evolution wavelet neural network (Ada-DEWNN) is proposed to improve the classification accuracy of the CAD system. The efficiency of the proposed system is tested on mammograms from the Mammographic Image Analysis Society (mini-MIAS) database using the leave-one-out cross validation as well as on mammograms from the Digital Database for Screening Mammography (DDSM) database using 10-fold cross validation. The proposed method on MIAS-database attains a fair accuracy of 0.8938 and AUC of 0.935 (95 % CI = 0.8213-0.9831). The proposed method is also tested for in-plane rotation and found to be highly rotation invariant. In addition, the proposed classifier is tested and compared with some well-known existing methods using receiver operating characteristic (ROC) analysis using DDSM- database. It is concluded the proposed classifier has better area under the curve (AUC) (0.9289) and highly précised with 95 % CI, 0.8216 to 0.9834 and 0.0384 standard error.
authors.affiliation: Department of Electrical Engineering, School of Engineering, Gautam Buddha University, Yamuna Expressway, Gautam Buddha Nagar, Greater Noida, India, 201308. satya002u@gmail.com.
authors.forename: Satya P
authors.identifier: http://orcid.org/0000-0003-3159-3622
authors.initials: SP
authors.lastname: Singh
authors.affiliation: Department of Electrical Engineering, School of Engineering, Gautam Buddha University, Yamuna Expressway, Gautam Buddha Nagar, Greater Noida, India, 201308. shabanaurooj@ieee.org.
authors.forename: Shabana
authors.initials: S
authors.lastname: Urooj
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008327:Mammography / Q000379:methods*; D016571:Neural Networks, Computer; D012372:ROC Curve; D058067:Wavelet Analysis
keywords: Breast cancer; Classification; Computer-aided diagnosis; Feature extraction; Generalized pseudo-Zernike moments