Indonesian Journal of Electrical Engineering and Computer Science Vol. 32, No. 1, October 2023, pp. 236~243 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v32.i1.pp236-243  236 Journal homepage: http://ijeecs.iaescore.com Breast cancer diagnosis based on support vector machine techniques Ruaa H. Mohammed Ameen 1 , Nasseer Moyasser Basheer 1 , Ahmed Khazal Younis 2 1 Department of Medical Instrumentation Technology Engineering, Technical Engineering College of Mosul, Northern Technical University, Mosul, Iraq 2 Department of the Computer Techniques Engineering, Technical Engineering College of Mosul, Northern Technical University, Mosul, Iraq Article Info ABSTRACT Article history: Received Feb 4, 2023 Revised Jun 9, 2023 Accepted Jun 17, 2023 In general, breast cancer is a fatal disease; however, early detection can significantly reduce the risk of death. A physician's experience in detecting and diagnosing breast саnсеr can be aided by automated feature extraction аnd classification procedures. Clinical exams and imaging studies are typically used to make a diagnosis of breast cancer. Mammography is by far the most common imaging technique used to detect the early warning signs of breast cancer. The goal of this paper is to design a computer-aided diagnosis/ detection (CAD) system by utilizing image processing techniques. These techniques will represent the first stage in the system, and they will significantly contribute to improving diagnostic accuracy. Next is the “Histogram of oriented gradients (HOG)” technique, which was used to extract features. The final stage involves applying machine learning techniques (MLT), in this case the support vector machine (SVM), a widely used method for detecting breast cancer using mammograms. In testing, the proposed model was found to be 94.74% accurate. Keywords: Breast cancer CAD system Image processing Machine learning Support vector machine This is an open access article under the CC BY-SA license. Corresponding Author: Ruaa H. Mohammed Ameen Department of Medical Instrumentation Technology Engineering, Technical Engineering College of Mosul, Northern Technical University Mosul, Iraq Email: ruaa.hassan1@ntu.edu.iq 1. INTRODUCTION Breast cancer is among the leading global causes of death for women. A higher mortality rate is caused by dense population, patients' ignorance of sickness symptoms, and their delayed seeking for medical advice. Additionally, the lack of medical experts and professionals in rural locations makes it more difficult to provide an early diagnosis accuratly [1]. Utilizing information systems and medical data to develop medical supporting systems that are able to aid the doctor in thinking about and identifying cases of breast cancer is one way that may be used to improve the early diagnosis of breast tumors. As a direct consequence of this, there is a higher probability of making a full recovery and a lower risk of passing away [2]. Imaging analysis is the greatest active method of detecting breast cancer. Many medical imaging methods used for diagnosis, including ultrasound (US), magnetic resonance imaging (MRI), digital mammography (DM), and Histopathological images. These are used to help doctors and radiologists to diagnose the condition [3]. The use of information technology is necessary since image interpretation is operator-dependent and needs expertise. This may speed the process and provide the specialists another perspective, improving the accuracy of the diagnosis [4].