Covid19 CT Image Classification Based on CNN Model

The COVID-19 pandemic, caused by the highly infectious SARS-CoV-2 virus, has underscored the urgent need for rapid and accurate diagnostic tools to alleviate healthcare resource constraints. This study presents a deep recognition model based on Convolutional Neural Networks (CNNs) for the classification of COVID-19 images. Utilizing a database of chest X-ray images, the model was trained and validated, achieving a classification accuracy of 89% on the test set. Our findings demonstrate the effectiveness of CNNs in extracting complex features, enhancing diagnostic efficiency, and reducing misdiagnoses and missed diagnoses in COVID-19 cases. Through comprehensive analysis of training and validation accuracy and loss curves, the model shows strong generalization capability without significant overfitting, aided by early stopping mechanisms.