Mammogram Learning System for Breast Cancer Diagnosis Using Deep Learning SVM

作     者：G.Jayandhi J.S.Leena Jasmine S.Mary Joans 

作者机构：Research ScholarAnna UniversityChennaiTamilnadu600025India Velammal Engineering CollegeChennaiTamilnadu600066India 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2022年第40卷第2期

页      面：491-503页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

主　　题：Deep learning architecture support vector machine breast cancer visual geometric group data augmentation 

摘      要：The most common form of cancer for women is breast *** advances in medical imaging technologies increase the use of digital mammograms to diagnose breast ***,an automated computerized system with high accuracy is *** this study,an efficient Deep Learning Architecture(DLA)with a Support Vector Machine(SVM)is designed for breast cancer *** combines the ideas from DLA with *** state-of-the-art Visual Geometric Group(VGG)architecture with 16 layers is employed in this study as it uses the small size of 3×3 convolution filters that reduces system *** softmax layer in VGG assumes that the training samples belong to exactly only one class,which is not valid in a real situation,such as in medical image *** overcome this situation,SVM is employed instead of the softmax layer in *** augmentation is also employed as DLA usually requires a large number of *** model with different SVM kernels is built to classify the *** show that the VGG-SVM model has good potential for the classification of Mammographic Image Analysis Society(MIAS)database images with an accuracy of 98.67%,sensitivity of 99.32%,and specificity of 98.34%.