Novel coronavirus pneumonia CT image classification based on inception ResNet

The novel coronavirus pneumonia is a highly contagious respiratory disease with a significant global impact. Traditional medical image analysis methods, relying on manual feature extraction and rule-based algorithms, often result in inconsistent and unreliable outcomes and struggle with the complexity and volume of medical imaging data. This research introduces a model based on the InceptionResNetV2 framework and Convolutional Neural Networks (CNNs) to automatically extract and analyze features from chest X-ray images, effectively classifying them into four categories: Normal, Viral Pneumonia, Lung Opacity (non-COVID pneumonia), and novel coronavirus pneumonia. The model achieves an impressive 95.16% accuracy and shows excellent performance in identifying novel coronavirus pneumonia cases with high precision and recall. Its application significantly enhances the efficiency and reliability of novel coronavirus pneumonia diagnosis in medical settings, thereby improving patient management and contributing to the containment of the pandemic.