Publication Date

2024

Document Type

Thesis

Committee Members

Lingwei Chen, Ph.D. (Advisor); Wen Zhang, Ph.D. (Committee Member); Krishnaprasad Thirunarayan, Ph.D. (Committee Member)

Degree Name

Master of Science (MS)

Abstract

The automated detection of pneumonia through chest X-ray presents a critical challenge in medical diagnostics, particularly due to the restrictions of limited and imbalanced chest X-ray data for training AI models. Traditional methods that depend on softmax confidence scores can be overconfident even when generating erroneous outputs especially when they are processing completely new inputs, leading to unreliable diagnostic results. This research addresses challenges in AI models which aim to develop a robust pneumonia detection system using an Energy-Based Out-of-Distribution (OOD) technique that can work effectively even with limited and imbalanced data. The study focused on creating a more reliable diagnostic framework that could maintain high accuracy and F1 scores even with limited training datasets. The proposed method uses energy scores derived from neural networks to distinguish between pneumonia (Out-of-Distribution) chest X-rays and non-pneumonia (In-Distribution) chest X-rays. Experiment is performed on chest X-ray datasets, comparing the performance against conventional softmax-based methods and other baseline approaches such as CNN, ResNet, DenseNet, and Outlier Exposure. The pneumonia detection using Energy-Based Out-of-Distribution approach showed superior performance even though it is trained only on 50 images, achieving a significant reduction in false negative rates while maintaining high accuracy and F1 score in pneumonia (Out-of-Distribution) cases. This research addresses the challenges of implementing deep learning neural networks in medical contexts with limited and imbalanced datasets.

Page Count

59

Department or Program

Department of Computer Science and Engineering

Year Degree Awarded

2024

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