XMP-Net:一种基于XAI的修改Xception模型,用于识别和其他皮肤疾病
Ferdib-Al-Islam1, Prithvi Biswas1, Partha Protim Gharami1
1Department of Computer Science and Engineering, Northern University of Business and Technology, Khulna, Bangladesh.
BioMed research international
|January 12, 2026
概括
一个新的深度学习模型,XMP-Net,准确地识别了水和其他皮肤疾病. 像Grad-CAM和LIME这样的可解释的AI方法提高了诊断信心,并为临床医生提供了视觉洞察力.
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:
- 准确及时诊断皮肤疾病,包括,对于公共卫生至关重要.
- 传统的诊断方法可能耗时,可能需要专门的专业知识.
- 人工智能 (AI) 的整合有可能提高诊断准确性和效率.
研究的目的:
- 开发和评估一个深度学习模型 (XMP-Net) 用于分类皮肤疾病:正常,水,麻疹和水.
- 使用Grad-CAM和LIME提高AI模型预测的可解释性.
- 评估模型在准确性,精度,回忆和F1得分方面的性能,特别是用于检测.
主要方法:
- 一个改进的基于Xception的深度学习架构,称为XMP-Net,被设计用于皮肤图像分类.
- 用Grad-CAM (梯度加权类激活映射) 和LIME (局部可解释的模型不可知解释) 来实现模型的可解释性.
- 该模型在一个包含正常皮肤,水,麻疹和水的图像的数据集上进行了训练和验证.
主要成果:
- XMP-Net实现了高分类准确度:正常皮肤的98.33%,水的98.25%,麻疹的84.21%,水的77.27%.
- 对于水,该模型的精度为91.80%,回忆率为98.25%,F1得分为94.92%.
- 来自Grad-CAM和LIME的视觉解释确定了影响预测的关键图像区域,提供了临床见解.
结论:
- XMP-Net显示了准确和可解释的皮肤状况诊断的巨大潜力,特别是麻疹.
- 可解释的人工智能 (XAI) 技术可以增加对人工智能驱动的诊断工具的信心.
- 开发的模型作为可访问,可靠的诊断工具的基础,特别是在资源有限的环境中.
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