Revolutionizing Healthcare Image Analysis in Pandemic-Based Fog-Cloud
  Computing Architectures
Al Zahraa Elsayed Khalil Mohamed Hany Harb
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.