Automated Pollen Recognition in Optical and Holographic Microscopy Images
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arXiv
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| Format: | Preprint |
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2025
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| author | Warshaneyan, Swarn Singh Ivanovs, Maksims Cugmas, Blaž Bērziņa, Inese Goldberga, Laura Tamosiunas, Mindaugas Kadiķis, Roberts |
| author_facet | Warshaneyan, Swarn Singh Ivanovs, Maksims Cugmas, Blaž Bērziņa, Inese Goldberga, Laura Tamosiunas, Mindaugas Kadiķis, Roberts |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08589 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated Pollen Recognition in Optical and Holographic Microscopy Images Warshaneyan, Swarn Singh Ivanovs, Maksims Cugmas, Blaž Bērziņa, Inese Goldberga, Laura Tamosiunas, Mindaugas Kadiķis, Roberts Computer Vision and Pattern Recognition Machine Learning Quantitative Methods I.2.6; I.2.10; I.4.6; I.4.8; I.4.9; I.5.4; J.3 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. |
| title | Automated Pollen Recognition in Optical and Holographic Microscopy Images |
| topic | Computer Vision and Pattern Recognition Machine Learning Quantitative Methods I.2.6; I.2.10; I.4.6; I.4.8; I.4.9; I.5.4; J.3 |
| url | https://arxiv.org/abs/2512.08589 |