Bone Fracture Detection using deep learning (Resnet50) - Final project in the fourth year of the degree

MediScan: AI-powered bone fracture detection system achieving 99.8% accuracy through deep learning. Features real-time X-ray analysis, transparent Grad-CAM visualizations, and clinical integration tools. Built with Python/FastAPI backend and responsive HTML/CSS frontend, making advanced medical diagnostics more accessible to healthcare providers.

This repository contains code the official code for the paper "Pediatric Wrist Fracture Detection in X-rays via YOLOv10 Algorithm and Dual Label Assignment System"

This project in a X-ray bone fracture detection App protoype to assist medical professionals to fast-forward the diagnosis process, it runs on the user's device.

Bone fracture detection in Xray images 🙋

Bone-fracture classification with a focus on robustness and interpretability.

AI-powered X-ray fracture detection using ResNet18 classification and YOLO segmentation with Streamlit interface

An AI-powered bone fracture detection system that analyzes X-ray images, predicts fracture presence with confidence scores, generates downloadable medical-style PDF reports, and delivers a clean, patient-friendly web interface using deep learning. Live link for this project given below

one Fracture Detection in X-ray Images using YOLOv8 — A deep learning project for medical imaging, fracture classification, and automated healthcare diagnosis.(this is a practice project )

Deep learning models for automated fracture detection and body part classification in musculoskeletal radiographs using the MURA dataset. Includes CNN, ResNet50, DenseNet169, and EfficientNet-B0 architectures in a multi-task learning setup.

🩻 Detect and segment fractures in X-ray images with this Streamlit app, enabling accurate analysis for medical professionals.