Abstract
Breast ultrasound examination is a routine, fast, and safe method for clinical diagnosis of breast tumors. In this paper, a classification method based on multi-features and support vector machines was proposed for breast tumor diagnosis. Multi-features are composed of characteristic features and deep learning features of breast tumor images. Initially, an improved level set algorithm was used to segment the lesion in breast ultrasound images, which provided an accurate calculation of characteristic features, such as orientation, edge indistinctness, characteristics of posterior shadowing region, and shape complexity. Simultaneously, we used transfer learning to construct a pretrained model as a feature extractor to extract the deep learning features of breast ultrasound images. Finally, the multi-features were fused and fed to support vector machine for the further classification of breast ultrasound images. The proposed model, when tested on unknown samples, provided a classification accuracy of 92.5% for cancerous and noncancerous tumors.
Highlights
International Agency for Research on Cancer (IARC) reported that breast cancer accounts for about 24.2% of cancers diagnosed in women worldwide [1]
We have used 1802 breast ultrasound (BUS) images that include 787 benign and 1015 malignant BUS images. It contains two parts. e first part is provided by Ultrasoundcases.info, which is a professional breast cancer ultrasound website developed by Hitachi Medical Systems in Switzerland and Dr Taco Geertsma, who works for Gelderse Vallei hospital in the Netherlands
We compared the characteristic features (CFs) computed from different BUS images, which showed that the CF designed in this paper can characterize the different properties of benign and malignant BUS images
Summary
International Agency for Research on Cancer (IARC) reported that breast cancer accounts for about 24.2% of cancers diagnosed in women worldwide [1]. It is the leading fatal cause in women, accounting for about 15%. With the development of modern medicine, if breast cancer is diagnosed early, the survival rate of patients will be significantly improved [2]. The Breast Imaging-Reporting and Data System (BI-RADS) [4] grades diagnosed by different clinicians for the same patient are subjective and different since some features in the breast ultrasound (BUS) images are not typically visible to diagnose [5]. Different breast lesions show different features in BUS images. Experience and the ability to understand the visual clues from BUS images are essential in reducing false negative detection. e count shows that the missed diagnosis of medical imaging in disease diagnosis can be between 10% and 30% [6]
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