Title: An automatic detection of microcalcification in mammogram images using neuro-fuzzy classifier
Authors: Neha N. Ganvir; Dinkar Manik Yadav
Addresses: Electronics and Telecommunication Engineering, Sinhgad Institute of Technology and Science (SITS), Pune, India ' GHRCOE, Pune, India
Abstract: Breast cancer is a standout amongst the most widely recognised diseases and has a high rate of mortality around the world, significantly risking the health of the females. The presence of microcalcifications (MCs) is an imperative sign of early breast cancer. This study proposes an automatic technique for detecting the microcalcifications in mammogram images. The images are filtered using anisotropic diffusion filter, segmented using the technique based on cellular automata and finally classified into benign, malignant and normal using a neuro-fuzzy classifier. For extensive experimental analysis, mini-MIAS database is considered with sensitivity, specificity and accuracy as evaluation parameters. From qualitative and quantitative results, it is evident that the proposed classification method has achieved significant and improved performance compared to existing state-of-the-art classification technique like SVM, ANN, etc.
Keywords: microcalcifications; mammogram; grey-level co-occurrence matrix; GLCM; cellular automata; neuro-fuzzy.
DOI: 10.1504/IJBET.2022.125573
International Journal of Biomedical Engineering and Technology, 2022 Vol.40 No.2, pp.130 - 145
Received: 27 Mar 2019
Accepted: 31 Mar 2020
Published online: 16 Sep 2022 *