用AI和物联网技术，98.88%准确率自动识别白血病细胞。
IoMT-based Automated Leukemia Classification using CNN and Higher Order Singular Value
- 结合CNN与高阶奇异值分解，实现细胞分类新方法。
- 在ALL-IDB2数据集上测试准确率达98.88%。
- 适合医疗影像分析、智能诊断系统开发者参考。
物联网（IoT）使物体具备身份并可联网通信，其在医疗领域的应用称为医疗物联网（IoMT）。急性淋巴细胞白血病（ALL）是一种血液系统癌症，起源于骨髓中未成熟白血球的过度增生，具有高转移性，若不及时诊断治疗将致命。目前病理学家依赖血涂片或骨髓涂片进行人工检查，但存在误判风险且耗时。为解决该问题，人工智能（AI）技术被用于从非癌组织中识别癌细胞。深度神经网络（DNN）通过多层结构提取高层次特征，是高效机器学习方法。本文提出一种基于卷积神经网络（CNN）与高阶奇异值分解（HOSVD）的新分类器，用于区分显微图像中的急性淋巴细胞白血病（ALL）细胞与正常细胞。模型部署于IoMT架构中，实现快速安全的白血病检测，并支持患者与医生实时交互。实验在急性淋巴细胞白血病图像数据库（ALL-IDB2）上进行，测试阶段平均准确率达到98.88%。
原文摘要 · Abstract (English)
The Internet of Things (IoT) is a concept by which objects find identity and can communicate with each other in a network. One of the applications of the IoT is in the field of medicine, which is called the Internet of Medical Things (IoMT). Acute Lymphocytic Leukemia (ALL) is a type of cancer categorized as a hematic disease. It usually begins in the bone marrow due to the overproduction of immature White Blood Cells (WBCs or leukocytes). Since it has a high rate of spread to other body organs, it is a fatal disease if not diagnosed and treated early. Therefore, for identifying cancerous (ALL) cells in medical diagnostic laboratories, blood, as well as bone marrow smears, are taken by pathologists. However, manual examinations face limitations due to human error risk and time-consuming procedures. So, to tackle the mentioned issues, methods based on Artificial Intelligence (AI), capable of identifying cancer from non-cancer tissue, seem vital. Deep Neural Networks (DNNs) are the most efficient machine learning (ML) methods. These techniques employ multiple layers to extract higher-level features from the raw input. In this paper, a Convolutional Neural Network (CNN) is applied along with a new type of classifier, Higher Order Singular Value Decomposition (HOSVD), to categorize ALL and normal (healthy) cells from microscopic blood images. We employed the model on IoMT structure to identify leukemia quickly and safely. With the help of this new leukemia classification framework, patients and clinicians can have real-time communication. The model was implemented on the Acute Lymphoblastic Leukemia Image Database (ALL-IDB2) and achieved an average accuracy of %98.88 in the test step.
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