用深度学习自动识别显微镜和全息图像中的花粉，提升兽医细胞学诊断效率。
Automated Pollen Recognition in Optical and Holographic Microscopy Images
- 采用YOLOv8s检测、MobileNetV3L分类，适配光学与全息显微图像。
- 光学图像上检测mAP50达91.3%，分类准确率97%；全息图经优化后性能显著提升。
- 低成本无透镜全息显微镜结合AI可实现可靠花粉分类，适合资源有限场景。
本研究探索深度学习在光学与全息显微图像中自动检测与分类花粉颗粒的应用，聚焦兽医细胞学场景。采用YOLOv8s进行目标检测，MobileNetV3L执行分类任务，在不同成像模态下评估性能。光学图像上检测mAP50达91.3%，分类总体准确率为97%；而原始灰度全息图像表现较差。通过自动化标注与边界框面积扩大进行数据集扩展，有效缩小性能差距：检测mAP50从2.49%提升至13.3%，分类准确率从42%增至54%。结果表明，结合深度学习技术，低成本无透镜数字全息显微设备可实现可靠的花粉分类任务。
原文摘要 · Abstract (English)
This study explores the application of deep learning to improve and automate pollen grain detection and classification in both optical and holographic microscopy images, with a particular focus on veterinary cytology use cases. We used YOLOv8s for object detection and MobileNetV3L for the classification task, evaluating their performance across imaging modalities. The models achieved 91.3% mAP50 for detection and 97% overall accuracy for classification on optical images, whereas the initial performance on greyscale holographic images was substantially lower. We addressed the performance gap issue through dataset expansion using automated labeling and bounding box area enlargement. These techniques, applied to holographic images, improved detection performance from 2.49% to 13.3% mAP50 and classification performance from 42% to 54%. Our work demonstrates that, at least for image classification tasks, it is possible to pair deep learning techniques with cost-effective lensless digital holographic microscopy devices.
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