Bone Age Assessment (BAA) Project

This project focuses on estimating bone age from X-ray images using machine learning models. We explore two different architectures: VGG16 and XceptionNet. The provided Jupyter notebooks contain code, data preprocessing steps, model training, and evaluation.

Table of Contents

• Introduction
• Installation
• Usage
• Models
• Contributing
• License

Introduction

The goal of this project is to estimate bone age from X-ray images using machine learning techniques. We explore two different architectures: VGG16 and XceptionNet. The provided notebooks contain code, data preprocessing steps, model architecture, and evaluation.

Installation

To set up the project, follow these steps:

1. Clone the repository: git clone https://github.com/nithin200417/BoneAgeAssessmentDL.git

Usage

Run the notebooks to explore the following models:

1. Bone Age Assessment using VGG16

• File: baa125156154.ipynb
• Description: This notebook demonstrates bone age assessment using VGG16 with feature extraction. It covers data loading, preprocessing, model architecture, and evaluation.

2. BAA using XceptionNet

• File: newmlbaa.ipynb
• Description: Explore BAA using XceptionNet. Compare it with VGG16, discuss differences, and evaluate performance.

3. Comparison of performances of different models in BAA

• File: deepmodelsbaa.ipynb

4. Performance of Densenet101, Resnet50, and Xceptionnet

• Files: deepbaa.ipynb, newdensenet.ipynb, baausingxception.ipynb, deeplove.ipynb

Contributing

Contributions are welcome! If you’d like to improve the project, report issues, or add new features, please provide your support by contacting me.