皮肤病学精度:为皮肤损伤分类开发最佳特征选择框架
Tallha Akram1, Riaz Junejo1, Anas Alsuhaibani2
1Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Cantt Campus, Islamabad 45040, Pakistan.
Diagnostics (Basel, Switzerland)
|September 9, 2023
概括
这项研究引入了一种使用深度学习和进化算法早期检测黑色素瘤的新方法. 该方法通过智能组合和选择多个模型中的特征来提高诊断准确性,从而改善黑色素瘤的识别.
科学领域:
- 皮肤病学 皮肤病学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 黑色素瘤是一种危险的皮肤癌,发病率越来越高.
- 早期检测显著提高了患者的生存率.
- 目前的基于计算机的诊断方法仍然存在局限性和错误率.
研究的目的:
- 开发一种基于计算机的先进方法,用于早期检测黑色素瘤.
- 通过结合深度学习模型来最大限度地利用功能信息.
- 使用进化特征选择技术来减少噪音和冗余.
主要方法:
- 使用深度模型 (Darknet53,DenseNet201,InceptionV3,InceptionResNetV2) 来进行特征提取.
- 应用转移学习以提高模型性能.
- 整合了来自多个模型的特征,并采用了新的控制灰狼优化 (ECGWO) 算法来进行特征选择.
- 验证了PH2,ISIC-MSK和ISIC-UDA皮肤学数据集的方法.
主要成果:
- 提出的方法有效地最大化了输入特征信息.
- 该ECGWO算法成功地减少了噪音和冗余的功能.
- 综合的融合和选择技术产生了高度分辨的特征信息.
- 该方法在基准数据集上表现出有效性,性能优于已有的技术.
结论:
- 结合深度模型和进化特征选择的新方法显示,对精确的黑色素瘤检测有很大的前景.
- 这种方法增强了特征的辨别力,从而提高了诊断准确度.
- 这项研究解决了用于医学图像分析的机器学习的一个关键研究挑战.
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