The emergence of pandemics has significantly emphasized the need for effective solutions in healthcare data analysis. One particular challenge in this domain is the manual examination of medical images, such as X-rays and CT scans. This process is time-consuming and involves the logistical complexities of transferring these images to centralized cloud computing servers. Additionally, the speed and accuracy of image analysis are vital for efficient healthcare image management. This research paper introduces an innovative healthcare architecture that tackles the challenges of analysis efficiency and accuracy by harnessing the capabilities of Artificial Intelligence (AI). Specifically, the proposed architecture utilizes fog computing and presents a modified Convolutional Neural Network (CNN) designed specifically for image analysis. Different architectures of CNN layers are thoroughly explored and evaluated to optimize overall performance. To demonstrate the effectiveness of the proposed approach, a dataset of X-ray images is utilized for analysis and evaluation. Comparative assessments are conducted against recent models such as VGG16, VGG19, MobileNet, and related research papers. Notably, the proposed approach achieves an exceptional accuracy rate of 99.88% in classifying normal cases, accompanied by a validation rate of 96.5%, precision and recall rates of 100%, and an F1 score of 100%. These results highlight the immense potential of fog computing and modified CNNs in revolutionizing healthcare image analysis and diagnosis, not only during pandemics but also in the future. By leveraging these technologies, healthcare professionals can enhance the efficiency and accuracy of medical image analysis, leading to improved patient care and outcomes.

翻译：疫情的出现显著提升了医疗数据分析领域对高效解决方案的需求。该领域面临的一大挑战是医学影像（如X光片和CT扫描）的人工检查。这一过程不仅耗时费力，还涉及将这些图像传输至集中式云服务器的物流复杂性。此外，图像分析的速度与准确性对于高效医疗图像管理至关重要。本研究提出了一种创新性的医疗架构，通过利用人工智能（AI）的能力来解决分析效率与准确性的挑战。具体而言，该架构采用雾计算技术，并设计了一种专门针对图像分析的改进型卷积神经网络（CNN）。研究对不同CNN层架构进行了深入探索与评估，以优化整体性能。为证明所提方法的有效性，采用X光图像数据集进行分析与评估，并与VGG16、VGG19、MobileNet等近期模型及相关研究论文进行了比较。值得注意的是，所提方法在正常病例分类中达到了99.88%的卓越准确率，配合96.5%的验证率、100%的精确率与召回率，以及100%的F1分数。这些结果凸显了雾计算与改进型CNN在革新医疗图像分析与诊断方面的巨大潜力——不仅适用于疫情期间，在长期发展中也具有显著价值。通过利用这些技术，医疗专业人员能够提升医学影像分析的效率与准确性，从而改善患者护理与治疗结果。