Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11235
Title: Classification of Breast Lesions on Mammogram Images using Monarch Butterfly Optimization and Support Vector Machine
Authors: Akkur, Erkan
Türk, Fuat
Eroğul, Osman
Keywords: Breast cancer
Gray level run matrix
Monarch Butterly optimization
Support vector machine
Publisher: Çankırı Karatekin University
Abstract: Currently, breast cancer affects many women worldwide. In recent years, many Computer-aided diagnosis (CAD) model have been developed for early diagnosis of breast cancer. An efficient CAD model is suggested to identify mammogram images as benign versus malignant in this study. The suggested CAD model constitutes four stages which are image acgusition, segmentation, feature extraction, feature selection and classification process. Gray level run matrix (GLRM) approach is used for feature extraction, while monarch butterfly optimization (MBO) for feature selection process. Support vector machine (SVM) algorithm is preferred for classification process. The suggested model has been tested on a private mammographic dataset. The suggested model (GLRM+MBO+SVM) shows an 0.944 of accuracy for breast lesion classification. Compared with similar studies, our proposed model showed good classification results for the breast lesion classification process.
URI: https://ikstc.karatekin.edu.tr/files/FullTextProceedingBook.pdf
https://hdl.handle.net/20.500.11851/11235
ISBN: 9786058291065
Appears in Collections:Biyomedikal Mühendisliği Bölümü / Department of Biomedical Engineering

Show full item record



CORE Recommender

Page view(s)

70
checked on Oct 7, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.