Detection and Classification of Breast Cancer in Mammography Images Using Pattern Recognition Methods
Publish place: Multidisciplinary Cancer Investigation، Vol: 3، Issue: 4
Publish Year: 1398
نوع سند: مقاله ژورنالی
زبان: English
View: 308
This Paper With 12 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_MCIJO-3-4_002
تاریخ نمایه سازی: 15 بهمن 1399
Abstract:
Introduction: In this paper, a method is presented to classify the breast cancer masses according to new geometric features.
Methods: After obtaining digital breast mammogram images from the digital database for screening mammography (DDSM), image preprocessing was performed. Then, by using image processing methods, an algorithm was developed for automatic extracting of masses from other normal parts of the breast image. In this study, 19 final different features of each image were extracted to generate the feature vector for classifier input. The proposed method not only determined the boundary of masses but also classified the type of masses such as benign and malignant ones. The neural network classification methods such as the radial basis function (RBF), probabilistic neural network (PNN), and multi-layer perceptron (MLP) as well as the Takagi-Sugeno-Kang (TSK) fuzzy classification, the binary statistic classifier, and the k-nearest neighbors (KNN) clustering algorithm were used for the final decision of mass class.
Results: The best results of the proposed method for accuracy, sensitivity, and specificity metrics were obtained 97%±4.36, 100%±0 and 96%±5.81, respectively for support vector machine (SVM) classifier.
Conclusions: By comparing the results of the proposed method with the results of the other previous methods, the efficiency of the proposed algorithm was reported.
Keywords:
Authors
Naser Safdarian
Department of Biomedical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran
Mohammadreza Hedyezadeh
Department of Biomedical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :