Detection and classification of normal and abnormal patterns in mammograms using deep neural network. (11th April 2019)

• Record Type: Journal Article
• Authors: Suresh, R.; Rao, A. Nagaraja; Reddy, B. Eswara
• Other Names: Karthikeyan M. guestEditor.; Chilamkurti Naveen guestEditor.; Li Xiong guestEditor.
• Is Part Of: Concurrency and computation. Volume 31:Number 14(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-04-11
• Subjects: Adaptively regularized kernel based fuzzy c means -- Deep neural network -- Histogram of oriented gradients -- Laplacian filtering; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5293
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Breast cancer detection is the most challenging aspect in the field of health monitoring system. In this paper, breast cancer detection was assessed by employing Mammographic Image Analysis Society (MIAS) dataset. The proposed approach contains four major steps, namely, image‐preprocessing, segmentation, feature extraction, and classification. Initially, Laplacian filtering was utilized to identify the area of edges in mammogram images and, also, it was very sensitive to noise. Then, segmentation was carried‐out using modified‐Adaptively Regularized Kernel‐based Fuzzy‐C‐Means (ARKFCM); it was a flexible high level machine learning technique to localize the object in complex template. In conventional ARKFCM, it was hard to segment the ill‐defined masses in mammogram images. To address this concern, the Euclidean distance in ARKFCM was replaced by correlation function in order to improve the segmentation efficiency. The hybrid feature extraction (Histogram of Oriented Gradients (HOG), homogeneity, and energy) was performed on the segmented cancer region to extract feature subsets. The respective feature values were given as the input for a multi‐objective classifier: Deep Neural Network (DNN) for classifying the normal and abnormal regions in mammogram images. The experimental outcome shows that the proposed methodology improved accuracy in breast cancer classification up to 3% to 9% compared to other existing methods.