A neural network for disease recognition of radiological images of pneumonia

Pneumonia ranks among the top causes of significant illnesses and fatalities globally, especially among the elderly and immunocompromised populations. The traditional diagnostic methods for pneumonia include X-ray and computed tomography imaging. Still, these methods have certain limitations, such as low image resolution and reliance on the experience of doctors for diagnosis. The application of artificial intelligence, especially neural network technology, in medical image analysis has provided new solutions for automatically detecting pneumonia. This article aims to explore a pneumonia image classification and recognition method based on neural network technology, analyse and compare the performance of convolutional neural network, visual geometry group, ResNet, and attention mechanism in pneumonia detection, and combine transfer learning to enhance the precision and reliability of detection even further. The experimental results show that the ResNet neural network combined with the attention mechanism performs the best in pneumonia image classification, with significantly improved accuracy and robustness. This study provides an efficient and accurate technical means for automatically detecting pneumonia, which has important clinical application value.