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.
- Introduction
- Dataset
- Data Pre-processing
- Data Augmentation
- Model Building
- Weighted Cross-Entropy
- Training
- Evaluation
- Making Predictions
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 numpyThe 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.
We used a ResNet-18 architecture for the image classification task. The model was trained using weighted cross-entropy to handle class imbalance effectively.
The model was trained on augmented data for a certain number of epochs. We monitored the AUC metric to evaluate model performance.
The final model achieved satisfactory results on the validation set with improved AUC values, indicating its effectiveness in identifying abnormalities.
We provided a method to make predictions using the trained model on new chest X-ray images.
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.