YOLO-Tryppa: A Novel YOLO-Based Approach for Rapid and Accurate Detection of Small Trypanosoma Parasites
Early detection of Trypanosoma parasites is critical for the prompt treatment of trypanosomiasis, a neglected tropical disease that poses severe health and socioeconomic challenges in affected regions. To address the limitations of traditional manual microscopy and prior automated methods, we propos...
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MDPI AG
2025-04-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/4/117 |
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| author | Davide Antonio Mura Luca Zedda Andrea Loddo Cecilia Di Ruberto |
| author_facet | Davide Antonio Mura Luca Zedda Andrea Loddo Cecilia Di Ruberto |
| author_sort | Davide Antonio Mura |
| collection | DOAJ |
| description | Early detection of Trypanosoma parasites is critical for the prompt treatment of trypanosomiasis, a neglected tropical disease that poses severe health and socioeconomic challenges in affected regions. To address the limitations of traditional manual microscopy and prior automated methods, we propose YOLO-Tryppa, a novel YOLO-based framework specifically engineered for the rapid and accurate detection of small Trypanosoma parasites in microscopy images. YOLO-Tryppa incorporates ghost convolutions to reduce computational complexity while maintaining robust feature extraction and introduces a dedicated P2 prediction head to improve the localization of small objects. By eliminating the redundant P5 prediction head, the proposed approach achieves a significantly lower parameter count and reduced GFLOPs. Experimental results on the public Tryp dataset demonstrate that YOLO-Tryppa outperforms the previous state of the art by achieving an AP50 of 71.3%, thereby setting a new benchmark for both accuracy and efficiency. These improvements make YOLO-Tryppa particularly well-suited for deployment in resource-constrained settings, facilitating more rapid and reliable diagnostic practices. |
| format | Article |
| id | doaj-art-103fd6cbb2a144ce9ef3db74edf38e7e |
| institution | DOAJ |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-103fd6cbb2a144ce9ef3db74edf38e7e2025-08-20T03:13:47ZengMDPI AGJournal of Imaging2313-433X2025-04-0111411710.3390/jimaging11040117YOLO-Tryppa: A Novel YOLO-Based Approach for Rapid and Accurate Detection of Small Trypanosoma ParasitesDavide Antonio Mura0Luca Zedda1Andrea Loddo2Cecilia Di Ruberto3Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, ItalyDepartment of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, ItalyDepartment of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, ItalyDepartment of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, ItalyEarly detection of Trypanosoma parasites is critical for the prompt treatment of trypanosomiasis, a neglected tropical disease that poses severe health and socioeconomic challenges in affected regions. To address the limitations of traditional manual microscopy and prior automated methods, we propose YOLO-Tryppa, a novel YOLO-based framework specifically engineered for the rapid and accurate detection of small Trypanosoma parasites in microscopy images. YOLO-Tryppa incorporates ghost convolutions to reduce computational complexity while maintaining robust feature extraction and introduces a dedicated P2 prediction head to improve the localization of small objects. By eliminating the redundant P5 prediction head, the proposed approach achieves a significantly lower parameter count and reduced GFLOPs. Experimental results on the public Tryp dataset demonstrate that YOLO-Tryppa outperforms the previous state of the art by achieving an AP50 of 71.3%, thereby setting a new benchmark for both accuracy and efficiency. These improvements make YOLO-Tryppa particularly well-suited for deployment in resource-constrained settings, facilitating more rapid and reliable diagnostic practices.https://www.mdpi.com/2313-433X/11/4/117computer visiondeep learningimage processingTrypanosoma detectionYOLO-based architectureslightweight detection models |
| spellingShingle | Davide Antonio Mura Luca Zedda Andrea Loddo Cecilia Di Ruberto YOLO-Tryppa: A Novel YOLO-Based Approach for Rapid and Accurate Detection of Small Trypanosoma Parasites Journal of Imaging computer vision deep learning image processing Trypanosoma detection YOLO-based architectures lightweight detection models |
| title | YOLO-Tryppa: A Novel YOLO-Based Approach for Rapid and Accurate Detection of Small Trypanosoma Parasites |
| title_full | YOLO-Tryppa: A Novel YOLO-Based Approach for Rapid and Accurate Detection of Small Trypanosoma Parasites |
| title_fullStr | YOLO-Tryppa: A Novel YOLO-Based Approach for Rapid and Accurate Detection of Small Trypanosoma Parasites |
| title_full_unstemmed | YOLO-Tryppa: A Novel YOLO-Based Approach for Rapid and Accurate Detection of Small Trypanosoma Parasites |
| title_short | YOLO-Tryppa: A Novel YOLO-Based Approach for Rapid and Accurate Detection of Small Trypanosoma Parasites |
| title_sort | yolo tryppa a novel yolo based approach for rapid and accurate detection of small trypanosoma parasites |
| topic | computer vision deep learning image processing Trypanosoma detection YOLO-based architectures lightweight detection models |
| url | https://www.mdpi.com/2313-433X/11/4/117 |
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