Automated pathological brain detection system: A fast discrete curvelet transform and probabilistic neural network based approach. (1st December 2017)

• Record Type: Journal Article
• Authors: Nayak, Deepak Ranjan; Dash, Ratnakar; Majhi, Banshidhar; Prasad, Vijendra
• Is Part Of: Expert systems with applications. Volume 88(2017)
• Page Start: 152
• Page End: 164
• Publication Date: 2017-12-01
• Subjects: Computer-aided diagnosis (CAD) -- Magnetic resonance imaging (MRI) -- Pulse-coupled neural network (PCNN) -- Fast discrete curvelet transform (FDCT) -- PCA+LDA -- Probabilistic neural network (PNN); Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.06.038
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The proposed scheme can efficiently detect pathological brain in real-time. FDCT via wrapping scheme is employed to capture curve features from MR images. The proposed PBDS is validated on several benchmark datasets. The proposed scheme outperforms 21 existing competent schemes. It has a potential to be installed on medical robots. Abstract: Computer-aided diagnosis (CAD) systems have drawn attention of researchers for arriving at qualitative and faster clinical decisions, and hence has become one of the most important directions of research. In this paper, we propose an efficient CAD system to classify pathological and healthy brains using brain MR images. The suggested pathological brain detection system (PBDS) has the ability to help radiologists to initiate the corrective measures for treating the ailing patients at an early stage. The proposed scheme uses a simplified pulse-coupled neural network (SPCNN) for the region of interest (ROI) segmentation and fast discrete curvelet transform (FDCT) for feature extraction. Subsequently, PCA+LDA approach is harnessed for feature dimensionality reduction and finally probabilistic neural network (PNN) is applied for classification. The scheme is validated on various standard datasets and compared with existing competent schemes with respect to classification accuracy and number of features. The statistical set up is kept similar as reported in the recent literature to derive an unbiased analysis. Experimental results demonstrate that the suggested scheme yields higher accuracy as compared to others with considerably less number of features. The number of parameters need to be tuned at different stages are significantly less in contrast to existing schemes. Further, PNN used has a simple network structure and offers faster learning speed. Therefore, the proposed scheme can effectively detect pathological brain in real-time and hence has a potential to be installed on medical robots.