A lightweight deep learning model for acute myeloid leukemia-related blast cell identification

Leukemia is a severe blood disorder that poses a threat to the life and well-being of patients. Accurate diagnosis of leukemia typing is crucial for treatment and prognosis. However, manual diagnosis requires significant resources and is subject to observer variation. Computer-aided diagnostic (CAD) technologies have emerged as a promising approach to complement manual diagnosis and alleviate the workload of specialized physicians. With the recent advances in deep learning, the performance of CAD has been substantially enhanced. In this paper, we propose a novel classification model, AMLNet, for acute myeloid leukemia (AML) cells. AMLNet is a lightweight model based on the ShuffleNet V2 architecture. We first validate the performance of the convolutional neural network and Transformer architecture models on the largest AML cell dataset to date. Then, we enhance the data augmentation approach and model structure of the ShuffleNet V2 model to develop AMLNet. The AUC, sensitivity, and accuracy of AMLNet are up to 0.9935%, 63.91%, and 96.43%, respectively, which achieves the optimal classification performance. Finally, the classification performance of AMLNet is verified on other public datasets, which is superior to other methods. AMLNet presents a promising solution for practical diagnosis scenarios since the end devices of assisted diagnosis often have limited computational power. The proposed AMLNet model effectively improves the classification performance of ShuffleNet V2 while offering the advantages of high performance and low computational consumption. Overall, our study demonstrates the potential of lightweight deep learning models in improving the accuracy of leukemia typing and reducing the workload of specialized physicians.