{"componentChunkName":"component---src-templates-blog-post-js","path":"/blog/multi-class-classification-of-breast-digital-pathology-image/","result":{"data":{"site":{"siteMetadata":{"title":"No Frills News"}},"contentfulNfnPost":{"postTitle":"Multi-class Classification of Breast Digital Pathology Image","slug":"multi-class-classification-of-breast-digital-pathology-image","createdLocal":"2021-06-10 14:31:15.452184","publishDate":"2021-06-10 00:00:00","feedName":"Image Recognition","sourceUrl":{"sourceUrl":"https://www.dovepress.com/deep-learning-based-multi-class-classification-of-breast-digital-patho-peer-reviewed-fulltext-article-CMAR"},"postSummary":{"childMarkdownRemark":{"html":"<p>In 2020, almost 1 in 4 of the newly diagnosed female cancer cases were breast cancer, and nearly 700,000 women died of breast cancer worldwide, accounting for 15.5% of female mortality.1 Therefore, early diagnosis of breast cancer plays a significant role in reducing cancer-related deaths.\nIn clinical practice, breast cancer is initially diagnosed by physical examination and medical imaging, including bilateral breast palpation, mammography, and ultrasonography.\nMaterials and MethodsA Deep Learning Framework for Multi-Class ClassificationIn our study, we proposed a deep learning approach designed for the analysis of breast cancer.\nThe classification of breast digital pathology images must rely on morphology-related features such as the density and variability of the nucleus.\nUnderstanding the evolution of the disease is critical for the prevention and treatment of breast cancer, especially for early diagnosis.</p>"}}}},"pageContext":{"slug":"multi-class-classification-of-breast-digital-pathology-image"}}}