TY -的A2纳齐尔沙非盟-比比,Nighat盟——Sikandar Misba AU - Ud Din,非盟的Ikram——Almogren Ahmad AU -阿里,Sikandar PY - 2020 DA - 2020/12/04 TI - IoMT-Based自动检测和分类的白血病使用深度学习SP - 6648574六世- 2020 AB -在过去的几年中,计算机辅助诊断(CAD)已迅速增加。许多机器学习算法已经被开发出来来识别不同的疾病，例如白血病。白血病是一种与白细胞(WBC)有关的疾病，影响骨髓和/或血液。快速、安全、准确的白血病早期诊断对于治疗和挽救患者的生命至关重要。根据发展情况，白血病有两种主要形式，即急性白血病和慢性白血病。每一种形式可以细分为髓系和淋巴系。因此，有四种白血病亚型。已经发展了各种方法来识别白血病的亚型。然而，就有效性、学习过程和绩效而言，这些方法都需要改进。该研究提供了一个基于医疗物联网(IoMT)的框架，以增强和提供快速和安全的白血病鉴定。 In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification. SN - 2040-2295 UR - https://doi.org/10.1155/2020/6648574 DO - 10.1155/2020/6648574 JF - Journal of Healthcare Engineering PB - Hindawi KW - ER -