Bone-Fracture-Detector

Project Name: Bone Fracture Detection System (Medical AI)

Description: Designed and deployed a deep learning-based web application to assist medical professionals in detecting bone fractures from X-ray images. The system leverages advanced Computer Vision techniques to provide rapid, automated diagnostic support with high accuracy.

Key Responsibilities & Tech Stack:

Deep Learning: Utilized Transfer Learning with architectures like ResNet/VGG16 (implemented in TensorFlow/Keras) to classify X-ray images.

Data Strategy: Applied extensive Image Preprocessing and Data Augmentation techniques (rotation, flipping, zooming) to overcome data scarcity and improve model generalization.

Model Optimization: Tuned hyperparameters and optimized the model to maximize Recall, ensuring minimal false negatives in diagnosis.

Deployment: Built an interactive, user-friendly frontend using Streamlit, allowing real-time image upload and inference.

Tools: Python, TensorFlow, Keras, OpenCV, Streamlit