The evaluation and diagnosis of cancer related diseases can be complex and lengthy. This is exacerbated due to manual analyses based on techniques that may take copious amount of time. In the last decade, different tools have been created to detect, analyze and classify different types of cancer in humans. However, there is still a lack of tools or models to automate the analysis of human cells to determine the presence of cancer. Such a model has the potential to improve early detection and prevention of said diseases, leading to more timely medical diagnoses. In this research, we present our current effort on the development of a Deep Learning Model capable of identifying blood cell anomalies. Our results show an accuracy that meets or exceeds the current state of the art, particularly achieving lower false negative rate in comparison to previous efforts reported.

中文翻译：

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通过卷积神经网络和迁移学习进行自动血细胞分类


癌症相关疾病的评估和诊断可能是复杂且漫长的。由于基于可能需要大量时间的技术的手动分析，这种情况变得更加严重。在过去的十年中，已经创建了不同的工具来检测、分析和分类人类不同类型的癌症。然而，仍然缺乏自动分析人体细胞以确定癌症是否存在的工具或模型。这种模型有可能改善上述疾病的早期检测和预防，从而实现更及时的医疗诊断。在这项研究中，我们展示了我们目前在开发能够识别血细胞异常的深度学习模型方面所做的努力。我们的结果显示，其准确性达到或超过了当前的技术水平，特别是与之前报告的工作相比，实现了更低的假阴性率。