一种基于增强的移动网络模型的行为性角质炎辅助诊断方法
Shiyang Li1, Chengquan Li2, Qicai Liu3,4
1School of Information Science and Engineering, Yunnan University, Kunming 650091, China.
Bioengineering (Basel, Switzerland)
|June 28, 2023
概括
这项研究开发了一种改进的MobileNet模型,用于准确诊断行为性角质炎 (AK). 人工智能模型实现了高精度和AUC,优于其他深度学习方法,用于高效的皮肤癌查.
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:
- 行动性角质炎 (AK) 是一种常见的癌前皮肤病变,经常被误诊.
- 目前的AK诊断依赖于临床专业知识和组织病理学,面临着由于类似外观的病变,如非行为性角质瘤 (NAK) 的挑战.
研究的目的:
- 通过深度学习开发一种高效,准确和自动化的AK诊断工具.
- 改进现有的深度学习模型,用于皮肤疾病诊断.
主要方法:
- 开发了一个改进的MobileNet卷积神经网络.
- 该模型在HAM10000数据集 (AK和NAK图像) 上进行训练和测试,并进行数据预处理.
- 对单独的数据集进行了外部验证,并将模型与ResNet,GoogleNet,EfficientNet和Xception进行了比较.
主要成果:
- 改进的MobileNet实现了0.9265的精度和0.97.97的ROC曲线下的面积 (AUC).
- 与其他评估的深度学习模型相比,它表现出卓越的性能.
- 该模型表现出最短的训练时间,五次交叉验证在821.7秒内完成.
结论:
- 拟议的方法提供了一个非常准确和稳定的方法来诊断AK.
- 这种人工智能工具可以提高医生的诊断效率和准确性,促进及时治疗患者.
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