Abnormal Breast Detection in Mammogram Images by Feed-forward Neural Network Trained by Jaya Algorithm

• Autorzy: Wang S. , Rao R. V. , Chen P. , Zhang Y. , Liu A. , Wei L.

Identyfikatory

DOI

10.3233/FI-2017-1487

• Języki publikacji: EN

Abstrakty

EN

(Aim) Abnormal breast can be diagnosed using the digital mammography. Traditional manual interpretation method cannot yield high accuracy. (Method) In this study, we proposed a novel computer-aided diagnosis system for detecting abnormal breasts in mammogram images. First, we segmented the region-of-interest. Next, the weighted-type fractional Fourier transform (WFRFT) was employed to obtain the unified time-frequency spectrum. Third, principal component analysis (PCA) was introduced and used to reduce the spectrum to only 18 principal components. Fourth, feed-forward neural network (FNN) was utilized to generate the classifier. Finally, a novel algorithm-specific parameter free approach, Jaya, was employed to train the classifier. (Results) Our proposed WFRFT + PCA + Jaya-FNN achieved sensitivity of 92.26% ± 3.44%, specificity of 92.28% ± 3.58%, and accuracy of 92.27% ± 3.49%. (Conclusions) The proposed CAD system is effective in detecting abnormal breasts and performs better than 5 state-of-the-art systems. Besides, Jaya is more effective in training FNN than BP, MBP, GA, SA, and PSO.

Słowa kluczowe

EN

fractional Fourier transform; abnormal breast detection; computer-aided diagnosis; mammogram; feedforward neural network; Jaya algorithm

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2017
• Tom: Vol. 151, nr 1/4
• Strony: 191--211
• Opis fizyczny: Bibliogr. 57 poz., fot., rys., tab., wykr.

Twórcy

autor

Wang S.

• School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China

autor

Rao R. V.

• Department of Mechanical Engineering, Sardar Vallabhbhai National Institute of Technology, Ichchanath, Surat-395 007, Gujarat State, India

autor

Chen P.

• Department of Electrical Engineering, Columbia University, New York, NY 10027, USA

autor

Zhang Y.

• Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu 210042, China

autor

Liu A.

• W.P. Carey School of Business, Arizona State University, Tempe, AZ 85287, USA

autor

Wei L.

• School of Electronic Information & Electrical Engineering, Shanghai Jiaotong University, Shanghai 200030, China

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