International Journal For Innovative Engineering and Management Research

Newpossibilities for automated evaluations and information extraction from massive collections of photographs have never existed before, thanks to the fast development of AI in imaging for medical purposes.It may be challenging to meet the computing needs of such cuttingedge AI technologies with on-premises capacities.Cloud computing promises very cheap access and flexibility.Using the cloud for healthcare image processing is an area where there is a dearth of research on the cost-benefit analysis.We explore the possibility of augmenting the National Lung Screening Trial (NLST) Computed Tomography (CT) images accessible via the NCI Imaging Data Commons (IDC) with artificial intelligence (AI) through the utilisation of computational resources provided by the cloud.To automate segmenting of images using TotalSegmentator and pyradiomics feature extraction for a massive cohort including over 126,000 CT volumes from over 26,000 consumers, we assessed the NCI Cancer Research Data Commons (CRDC) Cloud Resources -Terra (FireCloud) and Seven Bridges-Cancer Genomics Cloud (SB-CGC) platforms.In contrast to the predicted 522 days required on a single workstation, we were able to finish analysis in under 9 hours by using over 21,000 virtual machines (VMs) during the calculation.Using the cloud for this study came to a total of $1,011.05.