GAN-Assisted Sample Equalization for CNN-Based Human Peripheral Leukocyte Image Recognition and Classification
Shiwei Gao, Zerong Guo, Xin Meng, Liu Li, Youzhi Xiong, Sanshan Sun, Hui Li · 2024
Morphology analysis and the count of distinct cells in human peripheral blood are dominant methods to diagnose common blood diseases. In recent years, with the purpose of improving the efficiency of these methods, the convolutional neural network (CNN), a widely used model of artificial intelligence technology, has been utilized in computer-aided blood tests to recognize and classify blood cell images. However, due to the nature of a smaller number of leukocytes in one blood sample, the CNN model is generally trained by insufficient leukocyte images and thus shows lower accuracy on leukocytes. To address the above issue, we propose a generative adversarial network (GAN) assisted framework to increase leukocyte images and further enable the CNN model to improve the performance of human peripheral leukocyte image recognition and classification. We validate the effectiveness of the proposed framework through a comparative performance analysis of four CNN models trained by four image datasets deriving from different sample equalization methods. The numerical results show that the GAN-assisted sample equalization method is superior to making the CNN model converge fast and attain high recognition and classification accuracy.