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首页> 外文期刊>Innovation and research in biomedical engineering >Automatic Grading System for Diagnosis of Breast Cancer Exploiting Co-occurrence Shearlet Transform and Histogram Features
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Automatic Grading System for Diagnosis of Breast Cancer Exploiting Co-occurrence Shearlet Transform and Histogram Features

机译：乳腺癌诊断自动分级系统利用共进发生的剪切变换和直方图特征

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Objectives: Breast cancer (BC) is one of the most commonly reported health issues worldwide, especially in females. Early detection and diagnosis of BC can greatly reduce mortality rates. Samples obtained with different imaging methods such as mammography, computerized tomography, magnetic resonance, ultrasound, and biopsy are used in the diagnosis of BC. Histopathological images obtained from a biopsy contain vital information about the stage of the BC. Computer-aided systems are important tools to assist pathologists in the early detection of BC.Material and methods: In the current study, the use of gray-level co-occurrence matrix (GLCM) of Shearlet Transform (ST) coefficients were first scrutinized as textural features. ST is an advanced decomposition-based method that can analyze images in various directions and is sensitive to edge singularities. These features make ST more robust than other decomposition methods such as Fourier and wavelet. Color channel histogram features were also utilized for a second level of evaluation in the diagnosis of the BC stage. These features are considered one of the most important building blocks that pathologists consider in the course of grading histopathological images. Then, by combining these two features, the classification results were re-assessed utilizing Support Vector Machine (SVM) as a classifier.Results: The assessments were performed on a BreaKHis dataset containing benign and malignant histopathological samples. The average accuracy scores were reported as being 98.2%, 97.2%, 97.8%, and 97.3% in the sub-databases with 40x, 100x, 200x, and 400x magnification factors, respectively.Conclusions: The obtained results showed that the proposed method was quite efficient in histopathological image classification. Despite the relative simplicity of the approach, the obtained results were far superior to previously reported results. (c) 2020 AGBM. Published by Elsevier Masson SAS. All rights reserved.
机译：目的：乳腺癌（BC）是全球最常见的健康问题之一，特别是在女性中。 BC的早期检测和诊断可以大大降低死亡率。用不同的成像方法获得的样品，例如乳房X线摄影，计算机层面，磁共振，超声波和活组织检查，用于BC的诊断。从活组织检查获得的组织病理学图像含有关于BC阶段的重要信息。计算机辅助系统是协助病理学家在早期检测到BC的重要工具和方法：在目前的研究中，首先审查Shearlet变换（ST）系数的灰度共生矩阵（GLCM）的使用纹理特色。 ST是一种基于高级分解的方法，可以分析各种方向的图像，对边缘奇点敏感。这些功能使ST比其他分解方法更强大，例如傅立叶和小波。颜色通道直方图特征也用于在BC阶段的诊断中进行第二级评估。这些特征被认为是病理学家在分级组织病理学图像中考虑的最重要的构建块之一。然后，通过组合这两个特征，将分类结果利用支持向量机（SVM）作为分类器重新评估。结果：对含有良性和恶性组织病理学样品的断裂数据集进行评估。据报道平均精度评分为98.2％，97.2％，97.8％，分别为40倍，100x，200倍和400倍放大因子的子数据库中的97.3％。结论：所得的结果表明所提出的方法是在组织病理学图像分类中非常有效。尽管该方法的相对简单，所获得的结果远远优于先前报道的结果。 （c）2020年AGBM。由Elsevier Masson SA出版。版权所有。

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