论文标题
QRELU和M-QRELU：两个新型的量子激活功能以帮助医学诊断
QReLU and m-QReLU: Two novel quantum activation functions to aid medical diagnostics
论文作者
论文摘要
尽管尚未解决垂死的RELU问题，但RELU激活函数（AF）已广泛应用于图像分类，尤其是卷积神经网络（CNN），这对可靠的应用构成了挑战。这个问题显然对关键应用（例如医疗保健方面的应用程序）具有重要意义。最近的方法只是提出在同一未解决的垂死质量挑战中激活函数的变化。这项贡献通过调查了对Relu AF的创新量子方法的开发来报告不同的研究方向，从而避免了通过颠覆性设计避免垂死的归因问题。将泄漏的革命作为基线杠杆作用，在该基线上，将两个纠缠和叠加的量子原理应用于推导所提出的量子relu（QRELU）和修改的QRELU（M-QRELU）激活函数上。 QRELU和M-QRELU均在Tensorflow和Keras中实现并免费提供。这种原始方法在促进医学图像中促进Covid-19和帕金森氏病（PD）的案例研究中是有效的，并且得到了广泛的验证。在七个基准数据集上针对九个基于RELU的AF的两层CNN中评估了这两个新型AFS，包括从帕金森氏病和健康受试者的患者中通过图形片剂拍摄的螺旋绘画图像，以及在患有pneumonia and pneumonia and pneumonia and pneumonia and pneumonia and pneumonia and pneumonia and pneumonia and pneumonia and pneumonia and pneumonia and pneumonia and pneumonia and Pare-Pare超声图像。尽管计算成本较高，但结果表明，量子AFS在七个基准标记数据集中的五个中提出的总体分类准确性，精度，召回和F1得分，从而证明了其潜力是CNN中的新基准或金标准AF，并在关键应用中涉及的CNNS和辅助图像分类任务，例如COVID-COVID-CID-CID-119和PD的关键诊断。
The ReLU activation function (AF) has been extensively applied in deep neural networks, in particular Convolutional Neural Networks (CNN), for image classification despite its unresolved dying ReLU problem, which poses challenges to reliable applications. This issue has obvious important implications for critical applications, such as those in healthcare. Recent approaches are just proposing variations of the activation function within the same unresolved dying ReLU challenge. This contribution reports a different research direction by investigating the development of an innovative quantum approach to the ReLU AF that avoids the dying ReLU problem by disruptive design. The Leaky ReLU was leveraged as a baseline on which the two quantum principles of entanglement and superposition were applied to derive the proposed Quantum ReLU (QReLU) and the modified-QReLU (m-QReLU) activation functions. Both QReLU and m-QReLU are implemented and made freely available in TensorFlow and Keras. This original approach is effective and validated extensively in case studies that facilitate the detection of COVID-19 and Parkinson Disease (PD) from medical images. The two novel AFs were evaluated in a two-layered CNN against nine ReLU-based AFs on seven benchmark datasets, including images of spiral drawings taken via graphic tablets from patients with Parkinson Disease and healthy subjects, and point-of-care ultrasound images on the lungs of patients with COVID-19, those with pneumonia and healthy controls. Despite a higher computational cost, results indicated an overall higher classification accuracy, precision, recall and F1-score brought about by either quantum AFs on five of the seven bench-mark datasets, thus demonstrating its potential to be the new benchmark or gold standard AF in CNNs and aid image classification tasks involved in critical applications, such as medical diagnoses of COVID-19 and PD.
