传染病检测的神经网络：前景与挑战
Neural Networks for Infectious Diseases Detection: Prospects and Challenges
摘要：
人工神经网络（ANN）学习、纠正错误以及将大量原始数据转换成用于治疗和护理的有用医疗决策的能力已经增加了其用于增强患者安全性和护理质量的普及度。因此，本文综述了人工神经网络在为患者的医疗决策和有效的疾病诊断提供有价值的见解方面的关键作用。我们彻底审查了现有文献中提出的不同类型的人工神经网络，先进的人工神经网络适应复杂的应用。此外，我们还研究了ANN在各种疾病诊断和治疗方面的进展，如病毒，皮肤，癌症和COVID-19。此外，我们提出了一种新的深度卷积神经网络（CNN）模型ConXNet，用于提高COVID-19疾病的检测准确性。ConXNet使用不同的数据集进行训练和测试，其检测准确率和精度达到97%以上，明显优于现有模型。最后，我们强调了未来的研究方向和挑战，如算法的复杂性，可用数据不足，隐私和安全性，以及生物传感与人工神经网络的集成。这些研究方向需要相当大的关注，以提高医疗诊断和治疗应用的人工神经网络的范围。
英文摘要：
Artificial neural network (ANN) ability to learn, correct errors, and transform a large amount of raw data into useful medical decisions for treatment and care have increased its popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients' healthcare decisions and efficient disease diagnosis. We thoroughly review different types of ANNs presented in the existing literature that advanced ANNs adaptation for complex applications. Moreover, we also investigate ANN's advances for various disease diagnoses and treatments such as viral, skin, cancer, and COVID-19. Furthermore, we propose a novel deep Convolutional Neural Network (CNN) model called ConXNet for improving the detection accuracy of COVID-19 disease. ConXNet is trained and tested using different datasets, and it achieves more than 97% detection accuracy and precision, which is significantly better than existing models. Finally, we highlight future research directions and challenges such as complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.
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。
