Chest X-ray Disease Classification Using Convolutional Neural Networks

Overview

This project investigates the application of Convolutional Neural Networks (CNNs) for automating the classification of chest X-ray images into various disease categories. The goal is to enhance diagnostic accuracy and efficiency in medical imaging.

Dataset

The project leverages the NIH Chest X-ray Dataset, which contains over 112,000 labeled X-ray images. The dataset includes 14 disease categories such as:

• Atelectasis
• Cardiomegaly
• Consolidation
• Edema
• And more

Models

Several deep learning models were employed, including:

• Base CNN: A custom-designed convolutional neural network.
• VGG16: A pre-trained model known for its depth and performance.
• ResNet50: A deeper architecture with residual connections.
• MobileNetV2: A lightweight model optimized for mobile and resource-constrained environments.

Methodology

Data Preprocessing:

• Resizing images to 224x224 pixels.
• Normalizing pixel values.
• Applying data augmentation techniques such as rotation, flipping, zooming, and brightness adjustments.

Model Training:

• Using the Adam optimizer and binary cross-entropy loss for multi-label classification.
• Implementing early stopping and learning rate scheduling to prevent overfitting.

Evaluation Metrics:

• Accuracy
• AUC-ROC scores
• Precision, recall, and F1-score for each disease category

Results

• VGG16 achieved the highest accuracy of approximately 60%.
• ResNet50 and MobileNetV2 also performed well, with accuracies around 45% and 44%, respectively.
• The models faced challenges such as data imbalance and multi-label classification complexities.

Future Work

• Addressing data imbalance through techniques like SMOTE.
• Exploring advanced architectures like Vision Transformers.
• Enhancing model interpretability with Grad-CAM.
• Validating models on external datasets to ensure robustness and generalizability.

Conclusion

This project demonstrates the potential of CNNs in medical imaging but also highlights the need for further advancements to achieve clinically viable performance.

References

• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks.
• Tan, C., et al. (2018). A survey on deep transfer learning.
• Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning.
• Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Model-agnostic interpretability of machine learning.

Contact

For any questions or collaboration inquiries, please contact Sumanth Mylar at mylas02@pfw.edu.