A TWO-STAGE AUTOMATED FRAMEWORK FOR QUANTITATIVE MORPHOLOGICAL ANALYSIS OF MAST CELL DEGRANULATION IN HISTOPATHOLOGICAL IMAGES BASED ON YOLO AND CNN

Abstract

Quantitative analysis of mast cell (MC) morphology and degranulation states is crucial for assessing inflammatory responses and therapeutic efficacy in biomedical research. This study presents a novel two-stage deep learning framework for the automated quantitative morphological analysis of MC degranulation states in toluidine blue-stained histological sections. We constructed a specialized dataset of 1,054 rat tissue images. In the detection stage, YOLOv11m achieved superior performance with a mean average precision (mAP@0.5) of 84% for locating MCs amidst complex tissue backgrounds. In the classification stage, using the model we previously acquired to extract pure mast cell images, EfficientNetV2-S attained an accuracy of 89.6% ± 2.1% in discriminating degranulation states through fine-grained morphological analysis. Critically, Class Activation Mapping (CAM) visualization demonstrated that the model’s decision logic aligns precisely with pathological features of degranulation—such as membrane rupture and granule dispersal—thereby providing interpretable morphological evidence for automated classification. The proposed framework effectively decouples the tasks of cell localization and state classification, offering a robust, efficient, and morphologically interpretable solution for quantitative image analysis in histopathology. This approach has significant applications in acupuncture mechanism research and can be extended to other fields requiring granular structure analysis.