基于多功能融合和公英优化器的模型,用于自动诊断胃肠道疾病
Soner Kiziloluk1, Muhammed Yildirim1, Harun Bingol2
1Computer Engineering, Malatya Turgut Ozal University, Malatya, Turkey.
PeerJ. Computer science
|March 4, 2024
概括
一种新的人工智能 (AI) 混合方法使用深度学习和优化准确地分类胃肠疾病图像. 这种计算机辅助系统达到93.88%的准确性,改善了胃炎和息肉等疾病的诊断.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 胃肠道疾病如胃炎,反流和消化不良非常普遍.
- 症状重叠往往导致诊断混乱.
- 准确和快速的诊断对于有效的治疗和癌症预防至关重要.
研究的目的:
- 开发一种基于人工智能的新型混合方法,用于准确分类胃肠道图像.
- 为了提高解剖学标志,病理发现和息肉的诊断准确度.
- 改进胃肠道疾病的计算机辅助诊断系统.
主要方法:
- 使用预先训练的InceptionV3和MobileNetV2架构进行特征提取.
- 融合了两个架构的功能,以捕捉不同的图像特征.
- 使用花优化器 (DO) 进行特征选择和支持矢量机 (SVM) 进行分类.
主要成果:
- 拟议的混合方法实现了93.88%的高分类精度.
- 花优化器有效地减少了无关紧要和冗余的功能.
- 在实验比较中表现优于其他卷积神经网络 (CNN) 模型.
结论:
- 开发的AI混合方法为准确的胃肠图像分类提供了有希望的方法.
- 这种技术可以显著帮助更快,更精确地诊断胃肠道疾病.
- 该研究强调了将深度学习与医疗图像分析的元启发性优化整合在一起的潜力.
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