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

Issue title: Theory and Applications of Fractional Fourier Transform and its Variants

Guest editors: Yudong Zhang, Xiao-Jun Yang, Carlo Cattani, Zhengchao Dong, Ti-Fei Yuan and Liang-Xiu Han

Article type: Research Article

Authors: Wang, Shuihua^a | Rao, Ravipudi Venkata^b | Chen, Peng^c | Zhang, Yudong^{d; *} | Liu, Aijun^e | Wei, Ling^f

Affiliations: [a] School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China. [email protected] | [b] Department of Mechanical Engineering, Sardar Vallabhbhai National Institute of Technology, Ichchanath, Surat-395 007, Gujarat State, India | [c] Department of Electrical Engineering, Columbia University, New York, NY 10027, USA | [d] Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu 210042, China. [email protected] | [e] W.P. Carey School of Business, Arizona State University, Tempe, AZ 85287, USA | [f] School of Electronic Information & Electrical Engineering, Shanghai Jiaotong University, Shanghai 200030, China

Correspondence: [*] Address for correspondence: Jiangsu Key Laboratory of 3D, Printing Equipment and Manufacturing, Nanjing, Jiangsu 210042, China

Abstract: (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.

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

DOI: 10.3233/FI-2017-1487

Journal: Fundamenta Informaticae, vol. 151, no. 1-4, pp. 191-211, 2017