Computer aided diagnosis of cancer is a field of substantial worth in current scenario since approximately 38% population of the world is suffering from the disease. The detection of cancer is based on the observation of deformation in nuclei structure using histopathology slides/images. The proposed technique utilizes nuclei localization prior to classification of histopathology images as benign and malignant. The features used for classification are an ensemble of 150 bag of visual word features, extracted from preprocessed image and 20 handcrafted features, extracted from the internal parts of nuclei using localized histopathology images. The simulation results confirm the superiority of proposed localization based cancer classification method as compared to existing methods of the domain. It has reported average classification accuracy of 95.03% on BreakHis dataset.

中文翻译：

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用于癌症诊断的显微图像分类

癌症的计算机辅助诊断在当前情况下是一个非常有价值的领域，因为世界上大约 38% 的人口患有这种疾病。癌症的检测基于使用组织病理学幻灯片/图像观察细胞核结构的变形。所提出的技术在将组织病理学图像分类为良性和恶性之前利用细胞核定位。用于分类的特征是从预处理图像中提取的 150 个视觉词特征和使用局部组织病理学图像从细胞核内部提取的 20 个手工特征的集合。模拟结果证实了所提出的基于定位的癌症分类方法与该领域的现有方法相比的优越性。它报告的平均分类准确率为 95。