Detection and classification of normal and abnormal patterns in mammograms using deep neural network

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.

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Breast Cancer Detection Using Deep Belief Network by Applying Feature Extraction on Various Classifiers

“…The fundamental advancement behind this philosophy lies in the utilization of profound learn replica for the issue of mutually grouping unregistered mammogram sees and particular division guides of bosom sores (i.e., ample and mini calcifications).This is a comprehensive procedure that can arrange an entire mammographic test, hold the CC and MLO sees and the division maps, rather than the characterization of individual sores, which is the predominant methodology in the arena. The disadvantage is, the joint examination of unregistered multi-mode (CC and MLO) and multimodal input (pictures and division maps) needed elevated highlights [14].…”

Segmentation-Based Fractal Texture Analysis (SFTA) to Detect Mass in Mammogram Images

“…The results obtained an accuracy value of 80% for the classification of normal or abnormal mammogram images. Research by (Suresh et al, 2019) conducted segmentation with ARKFCM and hybrid for feature extraction with GLCM and Histogram of Oriented Gradients (HOG) with DNN classification obtained an accuracy value of 98.8%.…”

“…The previous study's weakness lies in the use of GLCM that extracted the value from the area of mass, and they only used four value features. Research (Suresh et al, 2019) conducted a hybrid GLCM with HOG to extract cancer cells' optimal feature value. The HOG calculates the gradient orientation and illumination of the edges or boundaries.…”