Red Blood Corpuscles called Erythrocytes are the most important element in blood composition which is mainly responsible in all living cells. To detect the cancer cell various methods are employed. In this paper, proper identification of cancer cells from unaffected RBCs are detected. The proposed novel method called Online Region Based Segmentation (ORBS) method is done that is used to find the regions of corpuscles. By using properties, metric is formulated for determination of shape which is abnormal in blood cells. Overall accuracy of 96.9% is obtained using proposed ORBS method and deep learning classification (DLC) method has accuracy of 97.1% that helps to diagnose cancer cells using feature extraction process done automatically. Sensitivity, specificity and precision values of the proposed segmentation method is found to be 96.7%, 95.6% and 98.4% respectively. The computation time was found as 22 seconds. Closeness of Proposed method in relative to True Positive values at the ROC curve indicates the performance as higher. Comparative analysis is made with ResNet-50 based on the different testing and training data at rate of 90%−10%, 80%−20% and 70%−30% respectively, which proves the robustness of proposed research work. Experimental results prove proposed system effectiveness compared with other detection methods.

中文翻译：

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基于深度学习的分类器在红细胞中早期癌症检测的分割技术-一种比较方法

称为红细胞的红血球是血液成分中最重要的元素，主要负责所有活细胞。为了检测癌细胞，采用了各种方法。在本文中，从未受影响的红细胞中正确识别了癌细胞。提出了一种新的方法，称为在线区域分割法（ORBS），该方法用于查找小体的区域。通过使用特性，制定度量标准来确定血细胞中异常的形状。使用提出的ORBS方法可获得96.9％的总体准确度，而深度学习分类（DLC）方法具有97.1％的准确度，有助于使用自动完成的特征提取过程诊断癌细胞。所提出的分割方法的敏感性，特异性和精确度值分别为96.7％，95。分别为6％和98.4％。计算时间为22秒。相对于ROC曲线的True正值，建议方法的接近度表明性能更高。使用ResNet-50根据不同的测试和训练数据分别进行了90％-10％，80％-20％和70％-30％的比较分析，证明了所提出研究工作的稳健性。实验结果证明了该系统与其他检测方法相比的有效性。这证明了拟议的研究工作的稳健性。实验结果证明了该系统与其他检测方法相比的有效性。这证明了拟议的研究工作的稳健性。实验结果证明了该系统与其他检测方法相比的有效性。