RETRACTED ARTICLE: A proposed framework for diagnosis and breast cancer detection

Abstract

This paper aims to early Breast Cancer (BC) detection by Mammography (MG) established on the production of excellent images and competent interpretation. This paper proposes two algorithms for Object Detection and Diagnosis (ODD) the BC. The first proposal is relied on merging the features of A Trous Algorithm (AT) with Homomorphic Processing (HP)Using Contrast Limited Histogram Equalization (AHUC) following by Segmentation and Feature Extraction (FE) for Classification (ASFC). The ASFC depends on enhancement using AHUC in addition to pre-processing followed by segmentation using Optimum Global Thresholding (OT) and finally FE for the detection or classification task. The second scheme is based segmentation after Hybrid structure HE and Fuzzy model (HEF) and finally FE for Classification (HEFC) The HEFC depends on improvement using HEF and pre-processing followed by segmentation using OT and finally FE for classification task. The performance quality metrics for the suggested techniques are entropy, average gradient, contrast, Sobel edge magnitude, homogeneity, sensitivity, specificity, precision and accuracy.Simulation results prove that the success of both techniques in detecting the BC objects. By comparing the first and the second presented algorithms, it is clear that the second suggested technique gives superior for the ODD the BC.