YOLO-based microglia activation state detection

Abstract

Recognition of microglia activation state is required in the research of problems such as brain neurological diseases. In this paper, a novel recognition network based on YOLOv5 is proposed for microglia activation state recognition. Firstly, the decoupled head is integrated into the head network, and secondly, novel feature extraction modules containing DenseNet are introduced: the DenseNet-C2f module and the DenseNet-SimCSPSPPF module. Subsequently, Wise-IoU is employed as the loss function, and the parameters therein are discussed. The network performance was evaluated using the microglia dataset. The experimental results show that the average precision of the enhanced network increases from 59.6 to 65.6%. In addition, the recall was improved from 56.3 to 71.5%. These improvements resulted in more efficient detection performance, which better meets the requirements of the medical field for identifying microglia activation states.