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相关概念视频

Skin Cancer01:30

Skin Cancer

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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相关实验视频

Updated: Jun 28, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

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使用统一深层卷积神经网络识别皮肤癌.

Nasser A AlSadhan1, Shatha Ali Alamri2, Mohamed Maher Ben Ismail1

  • 1Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia.

Cancers
|April 13, 2024
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概括
此摘要是机器生成的。

这项研究表明,YOLOv7在识别皮肤病变方面表现出色,优于其他模型. 这种人工智能工具有助于皮肤科医生在早期发现皮肤癌,可能减少不必要的活检.

关键词:
在 CAD 系统中使用 CAD 系统.癌症的识别 癌症的识别模式识别 模式识别 模式识别

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相关实验视频

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科学领域:

  • 皮肤病学 皮肤病学
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 全球皮肤癌发病率的上升需要改进的诊断工具.
  • 区分恶性黑色素瘤和良性病变是具有挑战性的,因为视觉上的相似性.
  • 基于图像的识别系统有可能帮助皮肤科医生,减少活检.

研究的目的:

  • 评估四个统一卷积神经网络 (YOLOv3,YOLOv4,YOLOv5,YOLOv7) 的皮肤病变分类的性能.
  • 根据病变定位,分类准确性和推断速度来比较这些模型.
  • 确定最有效的YOLO模型,以帮助早期皮肤癌诊断.

主要方法:

  • 在基准皮肤病变数据集上训练四个YOLO (你只看一次) 模型 (v3,v4,v5,v7).
  • 评估模型性能,使用诸如交叉点在联盟 (IoU),平均精度 (mAP) 和F1测量等指标.
  • 测量每个模型的推理时间,以评估实时适用性.

主要成果:

  • 在评估的模型中,YOLOv7表现出了卓越的性能.
  • YOLOv7实现了86.3%的IOU,75.4%的mAP和80%的F1测量.
  • YOLOv7的有效推断时间为每张图像0.32秒.

结论:

  • YOLOv7显示出作为皮肤科医生人工智能工具的巨大潜力.
  • 该模型可以帮助在皮肤癌的早期和准确诊断.
  • 实施YOLOv7可能有助于减少不必要的活检的数量.