基于YOLOv8的深度学习框架,用于使用口腔内图像检测和分类口腔潜在恶性疾病和口腔癌
Achla Bharti1, Nikita Kashyap1, Mala Kamboj1
1Department of Oral and Maxillofacial Pathology and Microbiology, Post Graduate Institute of Dental Sciences, Pandit BD Sharma University of Health Sciences, Rohtak, Haryana, India.
概括
使用YOLOv8的深度学习模型从口腔图像中准确检测口腔癌和潜在恶性疾病 (OPMDs). 这种人工智能工具可以帮助牙医在早期诊断和推及时治疗.
科学领域:
- 医疗保健中的人工智能
- 医学成像分析分析 医学成像分析
- 口腔病理学诊断 口腔病理学诊断
背景情况:
- 早期发现口腔潜在恶性疾病 (OPMDs) 和口腔癌对于改善患者的治疗结果至关重要.
- 数字口腔内成像为检查口腔组织提供了一种非侵入性方法.
- 对可疑病变进行准确的分类对于及时的临床管理至关重要.
研究的目的:
- 开发和评估使用YOLOv8实例分割架构的深度学习框架.
- 评估该模型在从数字口腔内图像中检测和分类OPMD和口腔癌方面的有效性.
主要方法:
- 一个由583张口腔内图像组成的数据集被增加到3,285张图像,包括口腔癌,OPMD和健康的粘膜.
- 使用Roboflow进行图像分类和注释,包括多类标签和实例分割.
- YOLOv8模型是通过专家口腔病理学家注释的数据进行训练和验证的.
主要成果:
- YOLOv8实例细分模型实现了高性能,mAP@50的98.83%和mAP@50-95的78.24%.
- 该模型表现出极好的分类准确性,精度为96.76%和回忆率为98.24%.
结论:
- 由人工智能驱动的YOLOv8细分模型显示了支持可疑口腔病变的检测和分类的巨大潜力.
- 这一框架可以帮助全科医生识别需要活检和转诊的OPMD和口腔癌病例.
- 该技术可以改善口腔病变的管理,特别是在资源有限的环境中.
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