Breast cancer diagnosis based on support vector machine techniques

Oct 1, 2023

Journal 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

publisher Northern Technical University /Technical Engineering College / Mosul

DOI 10.11591/ijeecs.v32.i1

Volume 32

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.

A survey: Breast Cancer Classification by Using Machine Learning Techniques

Sep 5, 2023

Journal NTU JOURNAL OF ENGINEERING AND TECHNOLOGY,

publisher Northern Technical University /Technical Engineering College / Mosul

Volume 2

Breast cancer in general is a common and a deadly disease. Early detection can significantly reduce the chances of death. Using automated feature extraction and classification algorithms, physician's experience in diagnosing and detecting breast cancer can be aided. This paper focuses on various statistical and machine learning(ML) studies of mammography datasets for enhancing the accuracy of breast cancer diagnosis and classification based on various variables. The Naïve Bayes,the K-nearest neighbors (KNN),the Support Vector Machine (SVM),the Random Forest (RF),the Logistic Regression(LR), Multilayer Perceptron (MLP), fuzzy classifier, and Convolutional Neural Network (CNN) classifiers, are the most widely used technologies in this field. This study provides an overview of the existing Computer-aided diagnosis/detection (CAD) systems based on artificial intelligence(AI) classification techniques and many types of medical image modalities. Potential research initiatives to build more efficient and accurate CAD systems have been investigated.