Automated Morphology Detection of Nail-Fold Capillaries Through Enhanced Object Detection Network
The analysis of nail-fold anatomy can effectively evaluate microcirculation and diagnose vascular-related diseases. Early detection of these conditions is crucial due to the risk of severe complications if intervention is delayed. Extensive research supports the notion that nail-fold capillary morph...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10813368/ |
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| author | Hang Thi Phuong Nguyen Hieyong Jeong |
| author_facet | Hang Thi Phuong Nguyen Hieyong Jeong |
| author_sort | Hang Thi Phuong Nguyen |
| collection | DOAJ |
| description | The analysis of nail-fold anatomy can effectively evaluate microcirculation and diagnose vascular-related diseases. Early detection of these conditions is crucial due to the risk of severe complications if intervention is delayed. Extensive research supports the notion that nail-fold capillary morphology serves as a critical biomarker for various disease processes, with the degree of capillary structural damage potentially reflecting the involvement of internal organs. This study proposes a non-invasive methodology for detecting nail-fold capillary morphology by integrating an object detection model for improvement within a deep learning framework. We conducted an ablation study to enhance YOLOv8’s performance in detecting nail-fold capillaries and classifying their morphology. Our enhancements included adding a detection layer to improve the detection of various-sized objects, implementing Efficient Channel Attention (ECA) mechanisms, and incorporating data augmentation techniques and hyper-parameter tuning. These modifications yielded a notable improvement in mean Average Precision at IoU 0.50 (mAP@50), with increases of 3.7% in mAP, 3.6% in precision, and 2.5% in recall compared to the baseline YOLOv8 model. This culminated in a mAP@50 score of 79.9%. We also utilized Slicing-Aided Hyperinference (SAHI) to enhance inference performance on untrained multi-scale images and smaller capillaries, demonstrating significant effectiveness in real-time testing scenarios. The results from this research are promising for advancing early-stage diabetes detection using nail-fold image analysis and could potentially enable real-time applications in clinical environments. |
| format | Article |
| id | doaj-art-854e692b19ea40ca8dbe89ea117b2634 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-854e692b19ea40ca8dbe89ea117b26342025-01-07T00:01:48ZengIEEEIEEE Access2169-35362025-01-01131697171310.1109/ACCESS.2024.352193710813368Automated Morphology Detection of Nail-Fold Capillaries Through Enhanced Object Detection NetworkHang Thi Phuong Nguyen0https://orcid.org/0000-0002-2016-9191Hieyong Jeong1https://orcid.org/0000-0002-8135-8252Department of Artificial Intelligence Convergence, Chonnam National University, Buk-gu, Gwangju, Republic of KoreaDepartment of Artificial Intelligence Convergence, Chonnam National University, Buk-gu, Gwangju, Republic of KoreaThe analysis of nail-fold anatomy can effectively evaluate microcirculation and diagnose vascular-related diseases. Early detection of these conditions is crucial due to the risk of severe complications if intervention is delayed. Extensive research supports the notion that nail-fold capillary morphology serves as a critical biomarker for various disease processes, with the degree of capillary structural damage potentially reflecting the involvement of internal organs. This study proposes a non-invasive methodology for detecting nail-fold capillary morphology by integrating an object detection model for improvement within a deep learning framework. We conducted an ablation study to enhance YOLOv8’s performance in detecting nail-fold capillaries and classifying their morphology. Our enhancements included adding a detection layer to improve the detection of various-sized objects, implementing Efficient Channel Attention (ECA) mechanisms, and incorporating data augmentation techniques and hyper-parameter tuning. These modifications yielded a notable improvement in mean Average Precision at IoU 0.50 (mAP@50), with increases of 3.7% in mAP, 3.6% in precision, and 2.5% in recall compared to the baseline YOLOv8 model. This culminated in a mAP@50 score of 79.9%. We also utilized Slicing-Aided Hyperinference (SAHI) to enhance inference performance on untrained multi-scale images and smaller capillaries, demonstrating significant effectiveness in real-time testing scenarios. The results from this research are promising for advancing early-stage diabetes detection using nail-fold image analysis and could potentially enable real-time applications in clinical environments.https://ieeexplore.ieee.org/document/10813368/Nail-fold capillariesYOLOv8ECA attentiondiabetescapillaroscopySAHI |
| spellingShingle | Hang Thi Phuong Nguyen Hieyong Jeong Automated Morphology Detection of Nail-Fold Capillaries Through Enhanced Object Detection Network IEEE Access Nail-fold capillaries YOLOv8 ECA attention diabetes capillaroscopy SAHI |
| title | Automated Morphology Detection of Nail-Fold Capillaries Through Enhanced Object Detection Network |
| title_full | Automated Morphology Detection of Nail-Fold Capillaries Through Enhanced Object Detection Network |
| title_fullStr | Automated Morphology Detection of Nail-Fold Capillaries Through Enhanced Object Detection Network |
| title_full_unstemmed | Automated Morphology Detection of Nail-Fold Capillaries Through Enhanced Object Detection Network |
| title_short | Automated Morphology Detection of Nail-Fold Capillaries Through Enhanced Object Detection Network |
| title_sort | automated morphology detection of nail fold capillaries through enhanced object detection network |
| topic | Nail-fold capillaries YOLOv8 ECA attention diabetes capillaroscopy SAHI |
| url | https://ieeexplore.ieee.org/document/10813368/ |
| work_keys_str_mv | AT hangthiphuongnguyen automatedmorphologydetectionofnailfoldcapillariesthroughenhancedobjectdetectionnetwork AT hieyongjeong automatedmorphologydetectionofnailfoldcapillariesthroughenhancedobjectdetectionnetwork |