International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Detection for Bone Cancer is a serious medical issue which demands immediate attention and intervention for better patient outcomes. Conventional methods have some serious limitations with regards to accessibility, cost-effectiveness, processing times, and availability on a global scale with respect to specific radiological knowledge and expertise. Raising awareness and promoting research for better AI-based medical technologies with significant societal benefits due to quick intervention and cost-effectiveness, we propose here an innovative dual-architecture AI system named ‘OsteoScan.AI’ combining ResNet18 Convolutional Neural Networks and Google Gemini Generative AI for holistic bone cancer examination. We propose an original seven-layer validation technique effectively rejecting images that are not medical images at all and relate to photography, selfies, and graphics before classification analysis. Utilizing pre-training with ImageNet pre-trained weights on ResNet18 Convolutional Neural Networks, we notice an outstanding accuracy rate of 95.2\% for classification of Bone Cancer from Bone X-Rays as Malignant and Normal classes. It can be effectively implemented as a complete-end stack online platform with React.js GUI implementation, Flask Web-Server implementation for backend with end-to-end processing below 1 second, Medical Image Authentication and Classification package with comprehensive classification and examination scan history, and an ‘AI-Counseling-System’ with an AI-powered chat platform for medical inquiries. EXPLANATION OF EXPERIMENTAL RESULTS clearly authenticates its efficacy and capabilities on strict medical examination criteria within 99.6\% rejection rate on ‘NON-MEDICAL-IMAGE’ classification. Both precision and accuracy with ‘EXPLANATIONS-IN-NATURAL-LANGUAAGE ’attempts to bridge an unsolved gap on critical usage and intervention with Medical AI technologies.

Keywords: Bone Cancer Detection, Deep Learning, ResNet18, Transfer Learning, Medical Image Analysis, Computer Vision, X-Ray Validation, Dual-AI System, Generative AI, Explainable AI, Responsible AI

Downloads: | DOI: 10.17148/IJARCCE.2025.141263