论文标题
使用深神经网络从微观血液涂片图像中自动检测急性淋巴细胞白血病亚型
Automated Detection of Acute Lymphoblastic Leukemia Subtypes from Microscopic Blood Smear Images using Deep Neural Networks
论文作者
论文摘要
估计每年诊断出300,000例新的白血病病例，占所有新癌症病例的2.8％，并且患病率每天上升。白血病最危险，最致命的类型是急性淋巴细胞白血病（所有），它影响了包括儿童和成人在内的所有年龄段的人。在这项研究中，我们提出了一个自动化系统，使用深神经网络（DNN）从微观血液涂片图像中检测所有形状的爆炸细胞。该系统可以以98％的精度检测所有细胞的多个亚型。此外，我们已经开发了一种远程诊断软件，以提供实时支持，以诊断微观血液涂片图像中的所有亚型。
An estimated 300,000 new cases of leukemia are diagnosed each year which is 2.8 percent of all new cancer cases and the prevalence is rising day by day. The most dangerous and deadly type of leukemia is acute lymphoblastic leukemia (ALL), which affects people of all age groups, including children and adults. In this study, we propose an automated system to detect various-shaped ALL blast cells from microscopic blood smears images using Deep Neural Networks (DNN). The system can detect multiple subtypes of ALL cells with an accuracy of 98 percent. Moreover, we have developed a telediagnosis software to provide real-time support to diagnose ALL subtypes from microscopic blood smears images.
