Pneumonia Image Classification based on Convolutional Neural Network
Published in 2022 IEEE 5th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), 2022
This paper introduces an improved convolutional neural network (CNN), which is used to classify and recognize different types of pneumonia using chest CT images. This classifying model is built and trained on thousands of real clinical chest CT images, which respectively belong to patients with viral pneumonia, patients with bacterial pneumonia, patients with COVID-19, and nonpatients. To richen the dataset and avoid over-fitting, pre-processing methods are recommended. Then the paper elaborates the structure of the new network and compares the performance of different optimizers in this dataset. Finally, the accuracy, specificity, precision, sensitivity, and F1-score of the model are calculated to quantitatively evaluate the performance of this model. The final training accuracy is about 97.9%, and the test accuracy is 91.8%.
Recommended citation: Y. Yulin, P. Yuhang, D. Jiakai, L. Jingyuan and T. T. Toe, "Pneumonia image classification based on convolutional neural network," 2022 IEEE 5th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), Shenyang, China, 2022, pp. 316-320, doi: 10.1109/AUTEEE56487.2022.9994305.
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