结肠癌疾病的诊断基于卷积神经网络和鱼虫优化器.
Amna Ali A Mohamed1, Aybaba Hançerlioğullari2, Javad Rahebi3
1Department of Material Science and Engineering, University of Kastamonu, Kastamonu 37150, Turkey.
Diagnostics (Basel, Switzerland)
|July 13, 2024
概括
这项研究介绍了一种使用卷积神经网络 (CNN) 和Fishier Mantis优化器用于准确检测结肠癌的AI驱动方法. 这种方法改进了传统方法,提高了早期诊断和患者的结果.
科学领域:
- 医学成像分析 医学成像分析
- 在瘤学中使用人工智能
- 生物启发的计算 生物启发的计算
背景情况:
- 传统的结肠癌诊断面临准确性和效率的局限性,阻碍了早期检测.
- 开发先进的计算方法对于提高诊断能力至关重要.
- 现有的深度学习模型 (如CNN) 是有前途的,但需要对复杂的医疗数据进行优化.
研究的目的:
- 开发和评估使用人工智能自动化结肠癌检测系统.
- 通过将深度学习与生物灵感优化相结合,提高结肠癌诊断的准确性和效率.
- 解决当前诊断模型中特征提取和分类准确性的局限性.
主要方法:
- 利用卷积神经网络 (CNN) 来从结肠癌图像中提取复杂的特征.
- 采用了Fishier Mantis优化器,一个生物灵感的算法,以微调CNN参数并减少功能.
- 将混合CNN-Fishier Mantis Optimizer模型与传统方法和其他优化技术 (例如遗传算法) 相比较.
主要成果:
- CNN-Fishier Mantis Optimizer模型在传统的诊断方法上表现出优异的性能.
- 在区分癌症和非癌症结肠组织方面取得了高的诊断准确度,灵敏度和特异性.
- 报告的关键性能指标包括94.87%的灵敏度,96.19%的特异性,97.65%的准确性和96.76%的F1-Score.
结论:
- 混合人工智能方法显著提升了结肠癌的计算机辅助诊断工具.
- 这种方法在改善早期检测率和患者预后方面具有重大前景.
- 生物灵感优化与深度学习的整合为复杂的医学图像分析提供了强大的解决方案.
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