PERFORMANCE EVALUATION OF DEEP LEARNING ALGORITHMS IN MEDICAL IMAGE PROCESSING

ABSTRACT Deep learning algorithms have revolutionized medical image processing, offering significant potential to enhance diagnostic accuracy, efficiency, and patient outcomes. Their application in tasks such as disease classification, segmentation, and anomaly detection is critical for advancing modern healthcare, particularly in resource-constrained settings where timely and accurate diagnoses are paramount. Despite their promise, existing studies often focus narrowly on accuracy metrics, overlooking robustness, interpretability, and computational efficiency. Additionally, many models are evaluated on limited or imbalanced datasets, raising concerns about their generalizability in real-world clinical scenarios. A comprehensive framework for evaluating these algorithms holistically is lacking. This study aims to evaluate the performance of deep learning algorithms—specifically VGG16, ResNet50, and Xception—in medical image processing, focusing on pneumonia detection from chest X-rays. The research seeks to compare their accuracy, precision, recall, and computational efficiency to identify the most effective model for clinical deployment. A comparative experimental approach was employed, utilizing a publicly available dataset of 5,863 pediatric chest X-rays. The models were trained and evaluated using standardized preprocessing techniques, including resizing, normalization, and data augmentation. Performance metrics such as accuracy, F1-score, AUC-ROC, and confusion matrices were analyzed to assess each model's effectiveness. The findings revealed that Xception outperformed VGG16 and ResNet50, achieving 99.04% accuracy, 99.37% F1-score, and 99.96% AUC-ROC, demonstrating superior diagnostic precision and robustness. ResNet50 exhibited high recall (99.49%) but lower precision (79.84%), while VGG16 balanced both metrics but required more computational resources. The results highlight Xception as a viable tool for clinical pneumonia detection, emphasizing its potential to improve diagnostic workflows. However, challenges such as dataset diversity and computational demands must be addressed for broader adoption. Future work should explore ensemble methods, model optimization for real-time use, and validation across diverse populations to enhance generalizability.