一个高效的人工子优化基于突变策略的皮肤癌预测
Mohamed Abd Elaziz1, Abdelghani Dahou2, Alhassan Mabrouk3
1Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt; Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt; Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates; Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon; MEU Research Unit, Middle East University, Amman 11831, Jordan.
Computers in biology and medicine
|June 26, 2023
概括
这项研究引入了一个新的深度学习模型,使用MobileNetV3和一个改进的人工子优化器 (IARO) 来提高皮肤癌的检测. 开发的方法显著提高了黑色素瘤诊断的准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 皮肤病学 皮肤病学
背景情况:
- 准确的黑色素瘤诊断对于早期检测至关重要,但目前的方法缺乏足够的准确性.
- 深度学习 (DL) 模型,特别是预训练的模型,有可能提高皮肤癌检测效率.
- 现有的DL方法通常需要从头开始培训,这限制了它们的效率.
研究的目的:
- 开发一个强大的深度学习模型来准确检测皮肤癌.
- 增强特征提取和选择,以提高诊断准确度.
- 在各种皮肤病变数据集上验证模型的性能.
主要方法:
- 利用MobileNetV3架构作为功能提取的深度学习骨干.
- 引入了改进的人工子优化器 (IARO) 具有高斯基基突变和交叉功能选择.
- 在PH2,ISIC-2016和HAM10000皮肤病变数据集上验证了模型.
主要成果:
- 实现了高准确率:PH2上的96.79%,ISIC-2016上的87.17%,HAM10000数据集上的88.71%.
- 证明IARO算法有效地忽略了不重要的特征,提高了预测.
- 结合DL和IARO方法显著提高了皮肤癌预测准确度.
结论:
- 与IARO合作开发的DL模型为皮肤癌检测提供了强大而准确的解决方案.
- 这种方法显示出早期黑色素瘤诊断和改善患者结果的巨大潜力.
- 该研究强调了将先进的DL架构与新的优化算法相结合的有效性.
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