一个用于皮肤癌分类的临床决策支持系统,使用分数子鱼启用集体分类器
Srilakshmi Cherukuri1, Srisailapu D Vara Prasad2
1Research scholar, Department of Computer Science and Engineering, GITAM Deemed to be University, Hyderabad, Telangana, 502329, India.
一个新型的 Fractional Gooseneck Barnacle Optimization-enabled Ensemble分类器可以改善皮肤癌的检测. 这种人工智能方法提高了早期诊断的准确性,解决了分类致命黑色素瘤亚型的挑战.
科学领域:
- 皮肤病学和人工智能研究
- 医学图像分析 医学图像分析
- 计算生物学 计算生物学
背景情况:
- 皮肤癌,特别是黑色素瘤,是全球重要的健康问题,诊断迟到,往往是由于缺乏认识和诊断挑战.
- 目前用于皮肤癌的诊断方法面临障碍,包括症状重叠与良性疾病,有限的皮肤病护理和高查成本.
- 准确和早期的皮肤癌分类对于有效的治疗和改善患者的结果至关重要.
研究的目的:
- 开发一种先进的自动化系统,使用新型集体分类器对皮肤癌进行分类.
- 通过克服图像分析和分类方面的现有挑战,提高皮肤癌诊断的准确性和效率.
- 引入一个新的优化技术,分数巴巴纳克优化 (FGBO),用于调整整合集分类器.
主要方法:
- 图像预处理包括无声化 (波形变换) 和细分 (基于特维斯基损失函数的综合注意力卷积神经网络).
- 图像增强技术 (翻转,裁剪),然后进行特征提取.
- 使用SpinalNet,量子扩展卷积神经网络 (QDCNN) 和Deep Kronecker网络 (DKN) 的合集分类,通过FGBO算法进行优化.
主要成果:
- 拟议的FGBO_Ensemble分类器实现了高性能指标:92.82%的准确性,89.94%的负预测值 (NPV),91.71%的正预测值 (PPV),93.54%的灵敏性,92.43%的特异性和92.61%的F1得分.
- 集成波形变换用于无声化和Tversky_CA-Net用于细分,有助于进行强大的图像分析.
- FGBO算法有效地调整了各种神经网络的组合,以实现最佳的皮肤癌分类.
结论:
- FGBO_Ensemble分类器展示了准确和早期皮肤癌检测的巨大潜力,解决了关键的诊断挑战.
- 该研究强调了将先进的深度学习架构与医疗图像分类的新型优化算法相结合的有效性.
- 这种人工智能驱动的方法可以通过促进皮肤癌及时诊断和治疗来改善患者的治疗结果.
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