Automatic identification of abnormal and normal cells is a critical step in computer-assisted pathology, owing to certain heterogeneous characteristics of cancer cells. However, automated nuclei detection is problematic in unevenly shaped, overlapping and touching nuclei. It is, consequently, essential to detect single and overlapping nuclei and distinguish them from single ones for a reasonable quantitative analysis. Diagnosis is improved by introducing a computer-aided diagnosis system to automatically detect breast cancer tissue nuclei from whole slide images of hematoxylin and eosin stains. We propose a method for the automatic cell nuclei detection, segmentation, and classification of breast cancer using a deep convolutional neural network (Deep-CNN) approach. The main contribution of this work is the detection of nuclei using anisotropic diffusion in a filter and applying a novel multilevel saliency nuclei detection model in ductal carcinoma of breast cancer tissue. The detected nuclei are classified into benign and malignant cells by applying the new Deep-CNN model. Finally, the novel multilevel saliency nuclei detection technique is integrated with the Deep-CNN to produce an nMSDeep-CNN model that turns out to be the most accurate results with very less computation time. The accuracy, sensitivity and specificity of the proposed system are 98.62%, 0.947 and 0.964, respectively. The classification for benign and malignant cells is evaluated by applying 10 fold cross-validation. Thus, the system can be clinically used for an objective, accurate, and rapid diagnosis of abnormal tissue. The effectiveness of the suggested framework is demonstrated through experiments on several datasets.

中文翻译：

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使用模糊合并技术的基于深度学习的乳腺癌检测和分类

由于癌细胞的某些异质性，自动识别异常和正常细胞是计算机辅助病理学中的关键步骤。但是，自动核检测在形状不均，重叠和接触核中存在问题。因此，必须检测单个和重叠的核并将它们与单个核区分开来进行合理的定量分析。通过引入计算机辅助诊断系统可以从苏木精和曙红染色的整个幻灯片图像中自动检测乳腺癌组织核，从而改善诊断水平。我们提出了一种使用深度卷积神经网络（Deep-CNN）方法对乳腺癌进行自动细胞核检测，分割和分类的方法。这项工作的主要贡献是利用在过滤器中的各向异性扩散来检测核，并在乳腺癌组织的导管癌中应用一种新颖的多级显着性核检测模型。通过应用新的Deep-CNN模型，将检测到的细胞核分为良性和恶性细胞。最后，将新颖的多级显着性核检测技术与Deep-CNN集成在一起，以生成nMSDeep-CNN模型，该模型证明是最准确的结果，而计算时间却非常短。该系统的准确性，敏感性和特异性分别为98.62％，0.947和0.964。通过应用10倍交叉验证来评估良性和恶性细胞的分类。因此，该系统可以在临床上用于客观，准确和快速地诊断异常组织。