Secure Cloud Enabled Platform for AI-Powered Computer Vision Syndrome Detection
DOI:
https://doi.org/10.63665/IJMEC.1102.03Keywords:
Syndrome Detection, Computer Vision, Secure Cloud Platform, Deep Learning, CNN, Federated LearningAbstract
Computer vision syndrome detection has become a significant discovery in the current healthcare system, as a way of diagnosing genetic and development disorders at an early stage with the help of facial features. Nevertheless, in practice, such systems must have high-level security, why privacy protection, and scalability in the case of sensitive patient information. This paper describes a safe cloud-based system that incorporates AI-based computer vision algorithms to detect syndromes automatically. The given framework makes use of using convolutional neural networks (CNNs) to extract facial features and make deep learning classifications using the Softmax probability-based syndrome prediction. Experimental evaluation was done using a 90 sample of faces images that consisted of Down Syndrome (33.3%), Williams Syndrome (22.2%), Turner Syndrome (16.7%), and other syndromes (27.8%). The gender representation was also fairly equal at 53.3/46.7 % males/females respectively. The site also has security features like encryption and privacy learning methods to secure the information of the patients in the cloud implementation.
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