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.

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

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.

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 )

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

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