Kumar Sanjay, Mallik Abhishek
Department of Computer Science and Engineering, Delhi Technological University, New Delhi, 110042 India.
Neural Process Lett. 2022 Oct 28:1-24. doi: 10.1007/s11063-022-11060-9.
The recent Coronavirus disease (COVID-19), which started in 2019, has spread across the globe and become a global pandemic. The efficient and effective COVID-19 detection using chest X-rays helps in early detection and curtailing the spread of the disease. In this paper, we propose a novel Trained Output-based Transfer Learning (TOTL) approach for COVID-19 detection from chest X-rays. We start by preprocessing the Chest X-rays of the patients with techniques like denoising, contrasting, segmentation. These processed images are then fed to several pre-trained transfer learning models like InceptionV3, InceptionResNetV2, Xception, MobileNet, ResNet50, ResNet50V2, VGG16, and VGG19. We fine-tune these models on the processed chest X-rays. Then we further train the outputs of these models using a deep neural network architecture to achieve enhanced performance and aggregate the capabilities of each of them. The proposed model has been tested on four recent COVID-19 chest X-rays datasets by computing several popular evaluation metrics. The performance of our model has also been compared with various deep transfer learning models and several contemporary COVID-19 detection methods. The obtained results demonstrate the efficiency and efficacy of our proposed model.
近期始于2019年的冠状病毒病（COVID-19）已在全球蔓延，成为一场全球大流行。利用胸部X光进行高效有效的COVID-19检测有助于早期发现并遏制该疾病的传播。在本文中，我们提出了一种新颖的基于训练输出的迁移学习（TOTL）方法，用于从胸部X光检测COVID-19。我们首先使用去噪、对比度调整、分割等技术对患者的胸部X光进行预处理。然后将这些处理后的图像输入到几个预训练的迁移学习模型中，如InceptionV3、InceptionResNetV2、Xception、MobileNet、ResNet50、ResNet50V2、VGG16和VGG19。我们在处理后的胸部X光上对这些模型进行微调。然后，我们使用深度神经网络架构进一步训练这些模型的输出，以实现性能提升并整合它们各自的能力。通过计算几个常用的评估指标，我们提出的模型在四个近期的COVID-19胸部X光数据集上进行了测试。我们模型的性能还与各种深度迁移学习模型以及几种当代的COVID-19检测方法进行了比较。获得的结果证明了我们提出的模型的效率和有效性。
