Performance analysis of deep learning approaches for pneumonia diagnosis using chest x-ray images

Diagnosis of pneumonia from Chest X-Ray images holds a significant place in clinical practice due to its low cost and widespread availability. However, achieving high performance with deep learning-based methods generally requires a large amount of labeled data, which constitutes a significant limitation in medical image analysis. In this thesis, the performance of deep learning and traditional machine learning approaches for diagnosing pneumonia from Chest X-Ray images under conditions of low-label data is comprehensively evaluated. For this purpose, a Hybrid Prototype Fusion Network (HPFN) developed based on the ConvNeXt-Tiny architecture is proposed, and deep feature representation and prototype-based similarity calculation are combined in a single decision mechanism. Furthermore, the proposed model is compared with Xception, VGG16, and ResNet50 architectures, and traditional approaches consisting of Support Vector Machine, Logistic Regression, and Extreme Gradient Boosting classifiers using SIFT, KAZE, and HOG features are also analyzed on the same dataset. All experiments were performed using the Chest X-Ray (Pneumonia) dataset, and the models were evaluated using accuracy, precision, sensitivity, and F1-score measures. Experimental results showed that the proposed HPFN model achieved 96.38% accuracy, 96.23% precision, 97.95% sensitivity, and 97.08% F1-score, outperforming both the compared deep learning architectures and traditional feature-based methods. Literature comparisons also revealed that the proposed approach is an effective computer-aided diagnostic method providing reliable, stable, and high-accuracy data under low-label data conditions.