期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2020
卷号：98
期号：15
页码：3116-3126
出版社：Journal of Theoretical and Applied
摘要：Image classification is helping radiologists to improve the accuracy of tumor detection in mammogram images for better diagnostics. The main aim of this proposed work is to build an efficient Neuro-Fuzzy support vector machine Techniques to detect and extract the tumor in the mammogram images and get an efficient result. The proffered classifiers are to achieve a very fast, simple, and efficient breast cancer diagnosis. The edge-based image segmentation and Neuro-fuzzy support vector machine are to find the Abnormality of classification such as cysts, calcification, fibro adenomas, and scar tissue. The tumor pixel values are calculated easily in a short time. Based on this experimental result, the overall performance of the proposed method is improved significantly. Furthermore, it can be inferred and ensures that the best classification accuracy of 99.85% ratio. And it has been compared in various Existing methods. There is no other research has been done for this type of research.
关键词：Neuro-Fuzzy support vector machine;Skeletonization;and Edge base Segmentation algorithm
