Research Article Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach Sushovan Chaudhury , 1 Manik Rakhra , 2 Naz Memon , 3 Kartik Sau , 4 and Melkamu Teshome Ayana 5 1 University of Engineering and Management, Kolkata, India 2 School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India 3 Mehran University of Science and Technology, Jamshoro, Pakistan 4 University of Engineering and Management, Kolkata, India 5 Department of Hydraulic and Water Resources Engineering, Arba Minch University, Ethiopia Correspondence should be addressed to Manik Rakhra; rakhramanik786@gmail.com and Melkamu Teshome Ayana; melkamu.teshome@amu.edu.et Received 27 August 2021; Revised 12 September 2021; Accepted 21 September 2021; Published 6 October 2021 Academic Editor: Deepika Koundal Copyright © 2021 Sushovan Chaudhury et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Breast cancer is a strong risk factor of cancer amongst women. One in eight women suffers from breast cancer. It is a life- threatening illness and is utterly dreadful. The root cause which is the breast cancer agent is still under research. There are, however, certain potentially dangerous factors like age, genetics, obesity, birth control, cigarettes, and tablets. Breast cancer is often a malignant tumor that begins in the breast cells and eventually spreads to the surrounding tissue. If detected early, the illness may be reversible. The probability of preservation diminishes as the number of measurements increases. Numerous imaging techniques are used to identify breast cancer. This research examines different breast cancer detection strategies via the use of imaging techniques, data mining techniques, and various characteristics, as well as a brief comparative analysis of the existing breast cancer detection system. Breast cancer mortality will be significantly reduced if it is identified and treated early. There are technological difficulties linked to scans and people’s inconsistency with breast cancer. In this study, we introduced a form of breast cancer diagnosis. There are different methods involved to collect and analyze details. In the preprocessing stage, the input data picture is filtered by using a window or by cropping. Segmentation can be performed using k-means algorithm. This study is aimed at identifying the calcifications found in bosom cancer in the last phase. The suggested approach is already implemented in MATLAB, and it produces reliable performance. 1. Introduction AI and machine learning are recently widely used in health care for the prediction of critical diseases like colorectal can- cer, Alzheimer, fetal brain abnormality detection, and type-2 diabetes risk prediction, and the present study used AI and ML for breast cancer prediction. Breast cancer is an abnormal development of malignant cells in the breast. Cancer spreads to other parts of the body if left untreated. Breast cancer, excluding skin cancer, is the most prevalent form of cancer among women in the United States, accounting for one in every three cancer diagnoses. In 2005, the United States was projected to have an esti- mated 211,240 new invasive cases of breast cancer among women. In 2005, about 1,690 additional male cases of breast cancer were anticipated. Breast cancer incidence increases beyond the age of 40. Women over the age of 50 have the greatest incidence (about 80% of invasive cases). Along with invasive breast cancer, women are projected to develop 58,590 new instances of in situ breast cancer in 2005. Around 88 percent of them will be diagnosed as ductal car- cinoma in situ (DCIS). DCIS instances are being detected as Hindawi Computational and Mathematical Methods in Medicine Volume 2021, Article ID 9905808, 13 pages https://doi.org/10.1155/2021/9905808