PDF(Pubmed)
Abstract：
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.
摘要：
急性淋巴细胞白血病,通常被称为所有，是一种可以影响血液和骨髓的癌症。诊断过程是一个困难的过程，因为它经常需要专家测试，比如验血,骨髓穿刺,还有活检,所有这些都非常耗时和昂贵。必须获得ALL的早期诊断，以便及时和适当地开始治疗。在最近的医学诊断中，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AI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,我们提出了一种新的基于深度学习的融合模型来检测所有类型的白血病。系统将诊断报告无缝地提供给集中式数据库，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的微观设备传输到云服务器。在云服务器中，配置了能够对PBS图像中的ALL进行分类的新融合模型。使用包括来自89个个体的6512个原始和分割图像的数据集来训练融合模型。在融合模型中，两个输入通道用于特征提取。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。两个输出特征合并在一起，和致密层用于白血病的分类。已经提出的融合模型获得了99.89%的准确率，精度为99.80%,召回率达到99.72%,这使它在白血病分类中处于很好的位置。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努力。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)。本申请旨在确定个体中是否存在白血病。这项研究的结果具有在生物医学研究中应用的巨大潜力，特别是提高计算机辅助白血病检测的准确性。
