Using a novel fractional-order gradient method for CNN back-propagation

• Main Authors: Taresh, Mundher Mohammed, Zhu, Ningbo, Ali, Talal Ahmed Ali, Alghaili, Mohammed, Guo, Weihua
• Format: Journal Article
• Language: English
• Published: 01-05-2022
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning
• DOI: 10.48550/arxiv.2205.00581

Computer-aided diagnosis tools have experienced rapid growth and development
in recent years. Among all, deep learning is the most sophisticated and popular
tool. In this paper, researchers propose a novel deep learning model and apply
it to COVID-19 diagnosis. Our model uses the tool of fractional calculus, which
has the potential to improve the performance of gradient methods. To this end,
the researcher proposes a fractional-order gradient method for the
back-propagation of convolutional neural networks based on the Caputo
definition. However, if only the first term of the infinite series of the
Caputo definition is used to approximate the fractional-order derivative, the
length of the memory is truncated. Therefore, the fractional-order gradient
(FGD) method with a fixed memory step and an adjustable number of terms is used
to update the weights of the layers. Experiments were performed on the COVIDx
dataset to demonstrate fast convergence, good accuracy, and the ability to
bypass the local optimal point. We also compared the performance of the
developed fractional-order neural networks and Integer-order neural networks.
The results confirmed the effectiveness of our proposed model in the diagnosis
of COVID-19.