使用自编码器自动化白血病诊断：一项比较研究
Automating Leukemia Diagnosis with Autoencoders: A Comparative Study
AI总结：
本文提出利用自编码器提取患者医疗数据特征以辅助白血病诊断，通过优化激活函数、优化器和网络架构，使所提模型在精确率和f1-score指标上比经典机器学习模型高出11%以上。
摘要：
白血病是最常见且危及人类生命的癌症类型之一。患者部分关键参数的医疗数据中隐藏着有价值的信息。对此，可利用深度学习提取这些信息。本文使用自编码器开发有价值的特征，以辅助提高白血病诊断的精度。研究尝试获取用于自编码器的最佳激活函数和优化器，并为该神经网络设计了最佳架构。将提出的架构与该领域的经典机器学习模型进行了比较。在精确率和f1-score指标上，我们提出的方法性能比其他机器学习方法高出11%以上。
英文摘要：
Leukemia is one of the most common and death-threatening types of cancer that threaten human life. Medical data from some of the patient's critical parameters contain valuable information hidden among these data. On this subject, deep learning can be used to extract this information. In this paper, AutoEncoders have been used to develop valuable features to help the precision of leukemia diagnosis. It has been attempted to get the best activation function and optimizer to use in AutoEncoder and designed the best architecture for this neural network. The proposed architecture is compared with this area's classical machine learning models. Our proposed method performs better than other machine learning in precision and f1-score metrics by more than 11%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
