Advancing Healthcare: Early Tuberculosis Detection in Chest X-Rays Through Select Convolutional Neural Networks
摘要
This paper introduces an innovative healthcare approach using convolutional neural networks (CNNs) to detect tuberculosis (TB) in chest X-rays. The primary objective is to develop an advanced neural network solution for TB diagnosis. This work employ ResNet50, VGG16, and MobileNet-V2 architectures to analyze a dataset of 4200 chest radiographs from 700 confirmed TB patients. The experiments show ResNet50’s superiority, achieving an impressive 93% accuracy with a 23% loss. This underscores ResNet50’s potential for early TB detection and its effectiveness in medical imaging. To attain these insights, the team extensively researched modern image classification techniques, object recognition methods, and transfer learning strategies. The CNN models were meticulously assessed deepening our understanding of their strengths and limitations. These findings hold profound implications for TB detection and significantly advance deep learning in medical imaging tasks.