Deep Learning-based Pneumonia Diagnosis: Evaluating CNN against Traditional Models

Abstract

This study explores the application and effect of an automatic diagnosis system for pneumonia Automatic detection and classification of cases. Experimental results show that compared with traditional methods such as support vector machine (SVM), random forest (RF) and multi-layer perceptron (MLP), CNN shows significant advantages in accuracy, recall and F1 score, especially since It has high accuracy and robustness in complex image feature extraction and lesion identification. This system can provide fast and reliable diagnostic support in resource-poor medical environments, significantly reducing missed diagnoses and misdiagnoses. During the experiment, we used data standardization and data enhancement techniques to improve the model's generalization ability under different image angles and resolutions. This study shows that an automatic diagnosis system based on deep learning can not only improve the efficiency of pneumonia detection, but also has the potential for practical clinical application, but its data dependence and computing resource requirements are also challenges. Future research will focus on model structure optimization, data set expansion and interpretability enhancement to further enhance the application value of deep learning technology in medical image analysis.