注意意识深度学习模型用于皮肤镜图像分类,用于皮肤疾病诊断
Malliga Subramanian1, Kogilavani Shanmugavadivel2, Sudha Thangaraj1
1Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India.
Current medical imaging
|April 22, 2025
概括
深度学习模型使用皮肤镜图像准确地分类皮肤疾病. 带有注意力机制的RegNetX实现了98.61%的准确性,有助于早期诊断皮肤病变.
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:
- 皮肤病变由于视觉相似而存在诊断挑战.
- 早期识别和治疗皮肤疾病对于预防严重健康问题至关重要.
研究的目的:
- 开发和评估深度学习模型,以使用皮肤镜图像准确地分类皮肤病变.
- 通过整合专注特征提取的注意力机制来提高模型性能.
主要方法:
- 使用了四个预训练的卷积神经网络 (CNN) 架构:RegNetX,EfficientNetB3,VGG19和ResNet-152.
- 将通道智能和空间注意力机制集成到CNN中,以改善对相关图像区域的关注.
- 使用贝叶斯优化优化模型超参数以提高性能.
主要成果:
- RegNetX表现出卓越的性能,在分类七种类型的皮肤疾病中达到98.61%的准确性.
- 整合注意力机制显著提高了模型在皮肤镜图像中识别关键特征的能力.
- 带有注意力机制的RegNetX表现出强的性能,突出了其在诊断任务中的有效性.
结论:
- 注意感知深度学习模型有效地根据皮肤镜像对皮肤疾病进行分类.
- 通过优化注意力机制增强的RegNetX模型,提供了准确而强大的诊断.
- 这项技术对于早期检测和治疗各种皮肤疾病至关重要.
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