Web-based AI Platform for Early Cancer Detection through Histopathological Image Analysis

Author(s)	Vaibhav Vudayagiri
Country	United States
Abstract	This article presents an innovative web-based artificial intelligence platform designed to revolutionize early cancer detection through advanced histopathological image analysis.The solution addresses critical challenges in traditional cancer diagnostics, where manual analysis faces limitations of inter-observer variability and time constraints. The platform leverages state-of-the-art convolutional neural networks, specifically a modified ResNet-152 architecture enhanced with attention mechanisms, to provide accurate and efficient cancer detection capabilities. The article demonstrates exceptional clinical performance, achieving 94.8% sensitivity (95% CI: 93.2-96.4%) and 92.3% specificity (95% CI: 90.7-93.9%) in comprehensive validation studies across five independent medical centers. This represents a 35% improvement in diagnostic accuracy compared to traditional methods. The platform processes high-resolution histopathological images (up to 100,000 x 100,000 pixels) with an average processing time of 45 seconds per case, enabling real-time analysis and rapid diagnosis
Keywords	Keywords: Histopathological Image Analysis; Convolutional Neural Networks; Cancer Detection; Digital Pathology; Healthcare Security
Field	Computer
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-12-04
DOI	https://doi.org/10.36948/ijfmr.2024.v06i06.32240

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research