sanjay-29-29 / bone-fracture-classification

Bone Fracture Classification Using CNN

This repository contains code and resources for a deep learning project aimed at classifying bone fractures using Convolutional Neural Networks (CNNs).

Introduction

The identification and classification of bone fractures are crucial in medical imaging for accurate diagnosis and treatment planning. This project leverages CNNs, a powerful deep learning technique, to automatically classify X-ray images of bone fractures.

Dataset

We utilize a curated dataset of X-ray images of bone fractures sourced from various medical databases. The dataset includes images of different types of fractures across diverse demographics, allowing the model to learn patterns and features for accurate classification.

Methodology

The core of this project revolves around the implementation of a CNN architecture tailored for bone fracture classification. The steps involve:

• Data preprocessing and augmentation to enhance the model's generalization
• Designing and training a CNN model using libraries like TensorFlow or PyTorch
• Evaluation of the model's performance using metrics such as accuracy, precision, recall, and F1-score
• Fine-tuning the model for better performance and robustness