An Improved Yolov10n for Detection of Bronchoalveolar Lavage Cells - Peihe Jiang; Shaoqi Li; Yanfen Lu; Xiaogang Song (IEEE Access)
Bronchoalveolar lavage fluid (BALF) is a liquid sample that reflects the biological status of lung tissues, containing a wealth of components such as cells and proteins. These components provide a non-invasive method to obtain pathological information about the lungs, serving as a powerful complement to traditional lung biopsies. However, the similarity in morphology and function of cells in BALF, combined with the diversity of sample processing and analysis methods, can lead to confusion in recognizing and distinguishing these cellular features. This study presents an improved Yolov10 method for the detection and classification of BALF cells, specifically targeting macrophages, lymphocytes, neutrophils, and eosinophils. The backbone network incorporates the PLWA module in place of the PSA module to enhance the acquisition of useful information, and the C2f-DC module replaces the C2f module to improve image feature extraction capabilities. Furthermore, the head network employs the Cross-Attention Fusion module (CAP) to enhance the retrieval of image information. Experimental results demonstrate that the model achieves a mean Average Precision (mAP) of 86.5% and a recall rate of 79.1%, confirming the model’s effectiveness.