Enhancing Healthcare Through Seeboneai an Advanced CNN and C-Means Algorithms for Automated Bone Fracture Detection

Abstract

Precision and swift diagnosis are paramount in addressing bone fractures. This study introduces "SeeBoneAI," an innovative system that leverages advanced Convolutional Neural Networks (CNNs) in combination with C-Means algorithms to automate the detection and classification of bone fractures in medical imaging, with a primary focus on X-ray images. SeeBoneAI transcends mere automation; it serves as a dependable tool for healthcare professionals. The system's fundamental components encompass image preprocessing, feature extraction, and classification. The initial preprocessing stage enhances X-ray images, optimizing image quality and reducing noise, laying a robust foundation for subsequent analysis. At the core of SeeBoneAI lies a sophisticated CNN model featuring multiple layers, which autonomously acquire and discern fracture-related patterns. The model training process utilizes a substantial dataset of annotated X-ray images, iteratively refining parameters and minimizing classification errors. After the training phase, the CNN model undergoes rigorous evaluation on a distinct dataset, assessing its performance across a spectrum of metrics, including accuracy and sensitivity. Unlike traditional edge detection techniques, SeeBoneAI's integrated CNN algorithms adeptly navigate multiresolution analysis and effectively mitigate noise interference. Research findings unequivocally affirm SeeBoneAI's proficiency in fracture detection across varying image resolutions and noise profiles, thereby elevating healthcare standards. This innovation stands as a vanguard in the realm of medical image analysis, holding the promise of redefining bone fracture diagnosis and treatment paradigms.