Novel Snapshot-Based Hyperspectral Conversion for Dermatological Lesion Detection via YOLO Object Detection Models
<b>Objective</b>: Skin lesions, including dermatofibroma, lichenoid lesions, and acrochordons, are increasingly prevalent worldwide and often require timely identification for effective clinical management. However, conventional RGB-based imaging can overlook subtle vascular characterist...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/12/7/714 |
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| Summary: | <b>Objective</b>: Skin lesions, including dermatofibroma, lichenoid lesions, and acrochordons, are increasingly prevalent worldwide and often require timely identification for effective clinical management. However, conventional RGB-based imaging can overlook subtle vascular characteristics, potentially delaying diagnosis. <b>Methods:</b> A novel spectrum-aided vision enhancer (SAVE) that transforms standard RGB images into simulated narrowband imaging representations in a single step was proposed. The performances of five cutting-edge object detectors, based on You Look Only Once (YOLOv11, YOLOv10, YOLOv9, YOLOv8, and YOLOv5) models, were assessed across three lesion categories using white-light imaging (WLI) and SAVE modalities. Each YOLO model was trained separately on SAVE and WLI images, and performance was measured using precision, recall, and F1 score. <b>Results:</b> Among all tested configurations, YOLOv10 attained the highest overall performance, particularly under the SAVE modality, demonstrating superior precision and recall across the majority of lesion types. YOLOv9 exhibited robust performance, especially for dermatofibroma detection under SAVE, albeit slightly lagging behind YOLOv10. Conversely, YOLOv11 underperformed on acrochordon detection (cumulative F1 = 65.73%), and YOLOv8 and YOLOv5 displayed lower accuracy and higher false-positive rates, especially in WLI mode. Although SAVE improved the performance of YOLOv8 and YOLOv5, their results remained below those of YOLOv10 and YOLOv9. <b>Conclusions:</b> Combining the SAVE modality with advanced YOLO-based object detectors, specifically YOLOv10 and YOLOv9, markedly enhances the accuracy of lesion detection compared to conventional WLI, facilitating expedited real-time dermatological screening. These findings indicate that integrating snapshot-based narrowband imaging with deep learning object detection models can improve early diagnosis and has potential applications in broader clinical contexts. |
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| ISSN: | 2306-5354 |