X-Ray Image Classification Project

This project aims to build a deep learning model for classifying chest X-ray images into "normal" and "abnormal" classes. The model is trained to detect abnormalities in X-ray images, making it a valuable tool for medical professionals and researchers.

Table of Contents

• Introduction
• Dataset
• Data Pre-processing
• Data Augmentation
• Model Building
• Weighted Cross-Entropy
• Training
• Evaluation
• Making Predictions

Installation

To run this project locally, you need the following prerequisites:

• Python 3
• TensorFlow
• Scikit-learn
• Matplotlib
• OpenCV
• Numpy

You can install the required packages using the following command:

pip install tensorflow scikit-learn matplotlib opencv-python numpy

Dataset

The dataset consists of grayscale chest X-ray images with two classes: "nofindings" and "effusion." We performed data augmentation to enrich the dataset and address class imbalance issues.

Model Building

We used a ResNet-18 architecture for the image classification task. The model was trained using weighted cross-entropy to handle class imbalance effectively.

Training

The model was trained on augmented data for a certain number of epochs. We monitored the AUC metric to evaluate model performance.

Evaluation

The final model achieved satisfactory results on the validation set with improved AUC values, indicating its effectiveness in identifying abnormalities.

Making Predictions

We provided a method to make predictions using the trained model on new chest X-ray images.

Conclusion

This project demonstrates the application of deep learning in medical image classification, specifically for chest X-ray images. The weighted cross-entropy technique proved crucial in handling imbalanced data and improving model accuracy in identifying abnormalities.