TY - JOUR ID - 129023 TI - Fuzzy Histogram Equalization and Computer Vision Automated Identification of Microcalcifications in Breast Cancer JO - The ISC International Journal of Information Security JA - ISECURE LA - en SN - 2008-2045 AU - S. Jumaa, Shereen AD - Al-Farahidi University, Baghdad, Iraq. Y1 - 2020 PY - 2020 VL - 12 IS - 3 SP - 73 EP - 79 KW - Fuzzy Histogram Equalization KW - computer vision KW - Automated Identification KW - Microcalcifications Breast Cancer DO - 10.22042/isecure.2021.271062.616 N2 - Simple signs existent in mammograms for diagnosing breast cancer are considered to be microcalcifications or MCs. Therefore, true detection of MCs is needed to minimize schedule diagnosis, efficient care and death rate due to breast cancer. A challenging task is to evaluate and interpret mammograms and, moreover to the poor contrast consistency of MCs relative to the remainder of the tissue, the precise identification of MCs, such as the minor size and random shape and size of the MC clusters, has several obstacles. These restrictions in the manual analysis of MCs increase the demand for an automated recognition system to help radiologists in mammogram analysis and it is important to design strength algorithm for this purpose. The goal of this paper is to present an efficient procedure that can be used to enhance images for extracting features to give excellent classification. The classifier senses which the region was normal, benign or malignant. The performance of KNN classifier with fuzzy histogram equalization using Otsu’s multi-threshold segmentation gives excellent results in detection and recognition in mammograms for breast cancer distinguished in image mammograms obtained from the hospital. UR - https://www.isecure-journal.com/article_129023.html L1 - https://www.isecure-journal.com/article_129023_c63648cf21f946c79ab880c32e790851.pdf ER -