Malaria cell classification with residual neural network

Xiangke Chen; Yixuan Chen; Jie Shen; Yiran Wang

doi:10.1117/12.2640989

13 October 2022 Malaria cell classification with residual neural network

Xiangke Chen, Yixuan Chen, Jie Shen, Yiran Wang

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122870S (2022) https://doi.org/10.1117/12.2640989
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Malaria is a transmittable disease caused by the parasites which belong to the Plasmodium family which is spread by the bite of the female mosquito. In the past, they were detected by trained microscopists who analyze microscopic blood smear images. However in recent years, a series of deep learning methods had been appeared. In this passage, we use ResNet with different parameters to classify Malaria cell images. We preprocess a dataset with more than 27,000 Malaria cell images from more than 350 patients. On this dataset, we train a convolutional neural network which outputs the classification accuracy and loss. By using transfer learning, we find the best network architecture in which we can classify the cells with the accuracy of 94.93%.

Citation Download Citation

Xiangke Chen, Yixuan Chen, Jie Shen, and Yiran Wang "Malaria cell classification with residual neural network", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122870S (13 October 2022); https://doi.org/10.1117/12.2640989

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