急性淋巴细胞白血病，通常称为 ALL，是一种可影响血液和骨髓的癌症。诊断过程是一个困难的过程，因为它经常需要专业测试，例如血液测试、骨髓抽吸和活检，所有这些都非常耗时且昂贵。为了及时、适当地开始治疗，获得 ALL 的早期诊断至关重要。在最近的医疗诊断中，通过人工智能（AI）和物联网（IoT）设备的集成取得了实质性进展。我们的提案引入了一种新的基于人工智能的医疗物联网 (IoMT) 框架，旨在从外周血涂片 (PBS) 图像中自动识别白血病。在这项研究中，我们提出了一种新颖的基于深度学习的融合模型来检测所有类型的白血病。该系统将诊断报告无缝传送到中央数据库，包括患者特定的设备。从医院采集血样后，PBS 图像通过支持 WiFi 的显微设备传输到云服务器。在云服务器中，配置了一个新的融合模型，能够对 PBS 图像中的 ALL 进行分类。融合模型使用包含来自 89 个人的 6512 张原始图像和分割图像的数据集进行训练。两个输入通道用于融合模型中的特征提取。这些通道包括原始图像和分割图像。 VGG16负责从原始图像中提取特征，而DenseNet-121负责从分割图像中提取特征。将两个输出特征合并在一起，并使用密集层对白血病进行分类。所提出的融合模型获得了 99.89% 的准确率、99.80% 的精确度和 99.72% 的召回率，这使其在白血病分类方面处于极好的位置。所提出的模型在性能方面优于几种最先进的卷积神经网络（CNN）模型。因此，这个提出的模型有可能挽救生命和精力。为了更全面地模拟整个方法，本研究开发了一个网络应用程序（测试版）。该应用程序旨在确定个体是否存在白血病。这项研究的结果在生物医学研究中具有巨大的应用潜力，特别是在提高计算机辅助白血病检测的准确性方面。

Acute lymphoblastic leukemia, commonly referred to as ALL, is a type of cancer that can affect both the blood and the bone marrow. The process of diagnosis is a difficult one since it often calls for specialist testing, such as blood tests, bone marrow aspiration, and biopsy, all of which are highly time-consuming and expensive. It is essential to obtain an early diagnosis of ALL in order to start therapy in a timely and suitable manner. In recent medical diagnostics, substantial progress has been achieved through the integration of artificial intelligence (AI) and Internet of Things (IoT) devices. Our proposal introduces a new AI-based Internet of Medical Things (IoMT) framework designed to automatically identify leukemia from peripheral blood smear (PBS) images. In this study, we present a novel deep learning-based fusion model to detect ALL types of leukemia. The system seamlessly delivers the diagnostic reports to the centralized database, inclusive of patient-specific devices. After collecting blood samples from the hospital, the PBS images are transmitted to the cloud server through a WiFi-enabled microscopic device. In the cloud server, a new fusion model that is capable of classifying ALL from PBS images is configured. The fusion model is trained using a dataset including 6512 original and segmented images from 89 individuals. Two input channels are used for the purpose of feature extraction in the fusion model. These channels include both the original and the segmented images. VGG16 is responsible for extracting features from the original images, whereas DenseNet-121 is responsible for extracting features from the segmented images. The two output features are merged together, and dense layers are used for the categorization of leukemia. The fusion model that has been suggested obtains an accuracy of 99.89%, a precision of 99.80%, and a recall of 99.72%, which places it in an excellent position for the categorization of leukemia. The proposed model outperformed several state-of-the-art Convolutional Neural Network (CNN) models in terms of performance. Consequently, this proposed model has the potential to save lives and effort. For a more comprehensive simulation of the entire methodology, a web application (Beta Version) has been developed in this study. This application is designed to determine the presence or absence of leukemia in individuals. The findings of this study hold significant potential for application in biomedical research, particularly in enhancing the accuracy of computer-aided leukemia detection.