使用YOLO框架检测食道癌症中的频谱辅助视觉增强器 (SAVE) 的评估
Chu-Kuang Chou1,2,3, Riya Karmakar4, Yu-Ming Tsao4
1Division of Gastroenterology and Hepatology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi 60002, Taiwan.
这项研究强调了YOLOv5框架的有效性,用于早期检测食道癌症的高光谱图像. 与YOLOv8.8相比,YOLOv5显著改善了状细胞癌和形症的识别.
科学领域:
- 医疗成像医学成像
- 在瘤学中使用人工智能
- 胃肠病学 胃肠病学
背景情况:
- 早期发现食道癌 (EC) 对于改善患者的治疗结果至关重要,但它仍然是一个重大挑战.
- 食道癌是全球癌症相关死亡的主要原因之一.
- 现有的检测方法往往难以识别早期恶性瘤.
研究的目的:
- 评估You Only Look Once (YOLO) 框架的性能,特别是YOLOv5和YOLOv8,用于早期检测食道癌症 (EC).
- 为了比较白光图像 (WLI) 与高光谱窄带图像 (HSI-NBI) 在检测生殖不良和状细胞癌 (SCC) 的有效性.
- 评估频谱辅助视觉增强器 (SAVE) 算法在将WLI转换为HSI-NBI方面的能力.
主要方法:
- 使用了来自Chia-Yi基督教医院的2741 WLI和2741 HSI-NBI数据集.
- 采用了YOLOv5和YOLOv8对象检测框架,对60%的数据进行了培训,对20%的数据进行了验证,并对20%的数据进行了测试.
- 使用精度,回忆,F1得分,平均平均精度 (mAP) 和混矩阵分析评估模型性能.
主要成果:
- 与标准RGB图像相比,HSI-NBI模型显示了SCC特征的优越学习能力.
- 在YOLO框架内,YOLOv5在功能学习和检测准确性方面表现优于YOLOv8.
- 与HSI-NBI相结合的YOLOv5模型在SCC诊断方面获得了最高的精度 (85.1%),在发育不良检测方面获得了52.5%的F1得分,明显优于YOLOv8.
结论:
- YOLOv5框架,特别是在利用SAVE算法生成的HSI-NBI时,为早期食道癌提供了显著改进的检测能力.
- 与传统的白光成像相比,高光谱成像提高了微妙的癌症特征的识别.
- 这种由人工智能驱动的方法对促进食道癌的早期诊断和治疗充满希望.
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