CLOUDVISION: AI-Driven Image Recognition Platform

Image recognition has evolved rapidly from rule-based systems to deep learning models, driven by the exponential growth in visual data and computing power. Traditional on-premise solutions struggle to meet the demands of large-scale, realtime image processing due to limitations in scalability, cost, and operational efficiency. This research addresses the challenge of building a scalable and cost-effective AI image recognition system by leveraging cloud infrastructure. The proposed method integrates deep learning-based image classification with Amazon Web Services (AWS), utilizing EC2 for computation, SQS for asynchronous task handling, and S3 for persistent storage within a modular, auto-scalable architecture. The system demonstrates high throughput, elastic resource management, and reliable classification accuracy across dynamic workloads. Results confirm enhanced performance, cost efficiency, and fault tolerance, making it a viable solution for diverse industries such as healthcare, security, and smart surveillance.

Authors

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-08-31
DOI
https://doi.org/10.22214/ijraset.2025.73951
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
0.00
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article

CLOUDVISION: AI-Driven Image Recognition Platform

Mrs. Elakia K
International Journal for Research in Applied Science and Engineering Technology
Brain Tumor Detection and Classification
article

CLOUDVISION: AI-Driven Image Recognition Platform

Mrs. Elakia K
article en

Abstract

Image recognition has evolved rapidly from rule-based systems to deep learning models, driven by the exponential growth in visual data and computing power. Traditional on-premise solutions struggle to meet the demands of large-scale, realtime image processing due to limitations in scalability, cost, and operational efficiency. This research addresses the challenge of building a scalable and cost-effective AI image recognition system by leveraging cloud infrastructure. The proposed method integrates deep learning-based image classification with Amazon Web Services (AWS), utilizing EC2 for computation, SQS for asynchronous task handling, and S3 for persistent storage within a modular, auto-scalable architecture. The system demonstrates high throughput, elastic resource management, and reliable classification accuracy across dynamic workloads. Results confirm enhanced performance, cost efficiency, and fault tolerance, making it a viable solution for diverse industries such as healthcare, security, and smart surveillance.

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(8)
Openalex Percentile: Top 71%
Brain Tumor Detection and Classification
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