Improving Tuberculosis Detection in Chest X-ray Images through Transfer Learning and Deep Learning: A Comparative Study of CNN Architectures

Abstract

AbstractIntroductionTuberculosis remains a significant global health challenge, necessitating more efficient and accurate diagnostic methods.MethodsThis study evaluates the performance of various convolutional neural network (CNN) architectures— VGG16, VGG19, ResNet50, ResNet101, ResNet152, and Inception-ResNet-V2—in classifying chest X-ray (CXR) images as either normal or TB-positive. The dataset comprised 4,200 CXR images, with 700 labeled as TB-positive and 3,500 as normal. We also examined the impact of data augmentation on model performance and analyzed the training times and the number of parameters for each architecture.ResultsOur results showed that VGG16 outperformed the other models across all evaluation metrics, achieving an accuracy of 99.4%, precision of 97.9%, recall of 98.6%, F1-score of 98.3%, and AUC-ROC of 98.25%. Surprisingly, data augmentation did not improve performance, suggesting that the original dataset’s diversity was sufficient. Furthermore, models with large numbers of parameters, such as ResNet152 and Inception-ResNet-V2, required longer training times without yielding proportionally better performance.DiscussionThese findings highlight the importance of selecting the appropriate model architecture based on task-specific requirements. While more complex models with larger parameter counts may seem advantageous, they do not necessarily offer superior performance and often come with increased computational costs.ConclusionThe study demonstrates the potential of simpler models such as VGG16 to effectively diagnose TB from CXR images, providing a balance between performance and computational efficiency. This insight can guide future research and practical implementations in medical image classification.