Breast cancer is one of the most significant medical problems of our time. Determining the appropriate methodologies for its early detection is still an open research problem in the scientific community. This research proposes a novel framework for automatically identifying and classifying breast cancer using MRI (Magnetic Resonance Imaging) images. The proposed approach utilizes automatic segmentation methods to detect suspicious areas in MRI images, features new feature extraction, and utilizes a variety of classification methods to create an automatic decision-making system that is able to classify the MRI images as benign or malign cancers. This research used MRI images of 56 patients from the medical imaging department of King Abdullah Medical City (KAMC), Saudi Arabia to assess the performance of the proposed framework. Our framework was able to achieve a classification accuracy of over 98% for its optimal configuration (SVM -linear kernel), while demonstrating excellent false-positive and false negative rates, sensitivity and specificity (0%,15%, 97%, 100% respectively).

乳腺癌是我们这个时代最重要的医学问题之一。为其早期检测确定适当的方法仍然是科学界的一个开放研究问题。这项研究提出了一种使用 MRI（磁共振成像）图像自动识别和分类乳腺癌的新框架。所提出的方法利用自动分割方法来检测 MRI 图像中的可疑区域，提取新的特征，并利用各种分类方法来创建能够将 MRI 图像分类为良性或恶性癌症的自动决策系统。这项研究使用了来自沙特阿拉伯阿卜杜拉国王医疗城 (KAMC) 医学成像部门的 56 名患者的 MRI 图像来评估拟议框架的性能。