Department of Electrical and Computer Engineering (ECE), King Abdulaziz University, Jeddah, Saudi Arabia.
Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah, Saudi Arabia.
Contrast Media Mol Imaging. 2022 Sep 15;2022:5297709. doi: 10.1155/2022/5297709. eCollection 2022.
Coronavirus 2019 (COVID-19) has become a pandemic. The seriousness of COVID-19 can be realized from the number of victims worldwide and large number of deaths. This paper presents an efficient deep semantic segmentation network (DeepLabv3Plus). Initially, the dynamic adaptive histogram equalization is utilized to enhance the images. Data augmentation techniques are then used to augment the enhanced images. The second stage builds a custom convolutional neural network model using several pretrained ImageNet models and compares them to repeatedly trim the best-performing models to reduce complexity and improve memory efficiency. Several experiments were done using different techniques and parameters. Furthermore, the proposed model achieved an average accuracy of 99.6% and an area under the curve of 0.996 in the COVID-19 detection. This paper will discuss how to train a customized smart convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.
2019 年冠状病毒（COVID-19）已成为大流行。从全球受害者人数和大量死亡人数可以看出 COVID-19 的严重性。本文提出了一种有效的深度语义分割网络（DeepLabv3Plus）。首先，利用动态自适应直方图均衡化来增强图像。然后使用数据增强技术来增强增强后的图像。第二阶段使用几种预训练的 ImageNet 模型构建自定义卷积神经网络模型，并将它们进行比较，以反复修剪性能最佳的模型，从而降低复杂性并提高内存效率。使用不同的技术和参数进行了多次实验。此外，所提出的模型在 COVID-19 检测中达到了 99.6%的平均准确率和 0.996 的曲线下面积。本文将讨论如何使用一组胸部 X 射线在各种参数下训练准确率为 99.6%的定制智能卷积神经网络。
