Background. Coronavirus disease (COVID-19) is an infectious illness that spreads widely over a short period of time and finally causes a pandemic. Unfortunately, the lack of radiologists, improper COVID-19 diagnosing procedures, and insufficient medical supplies have all played roles in these devastating losses of life. Deep learning (DL) could be used to detect and classify COVID-19 for potential image-based diagnosis. Materials and Methods. This paper proposes an improved deep convolutional neural network (IDConv-Net) to detect and classify COVID-19 using X-ray and computed tomography (CT) images. Before the training phase, preprocessing methods such as filtering, data normalization, classification variable encoding, and data augmentation were used in conjunction with the proposed IDConv-Net to increase the effectiveness of the detection and classification processes. To extract essential features, deep CNN is then employed. As a result, the suggested model can identify patterns and relationships crucial to the image classification task, resulting in more precise and useful diagnoses. Python and Keras (with TensorFlow as a backend) were used to carry out the experiment. Results. The proposed IDConv-Net was tested using chest X-rays and CT images collected from hospitals in Sao Paulo, Brazil, and online databases. After evaluating the model, the proposed IDConv-Net achieved an accuracy of 99.53% and 98.41% in training and testing for CT images and 97.49% and 96.99% in training and testing for X-ray images, respectively. Further, the area under the curve (AUC) value is 0.954 and 0.996 for X-ray and CT images, respectively, indicating the excellent performance of the proposed model. Conclusion. The findings of our proposed IDConv-Net model confirm that the model outperformed compared to existing COVID-19 detection and classification models. The IDConv-Net outperforms current state-of-the-art models by 2.25% for X-rays and 2.81% for CT images. Additionally, the IDConv-Net training approach is significantly quicker than the current transfer learning models.

中文翻译：

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使用胸部 X 射线和 CT 图像的深度卷积神经网络进行 COVID-19 检测和分类的增强技术

背景。冠状病毒病（COVID-19）是一种在短时间内广泛传播并最终引起大流行的传染病。不幸的是，放射科医生的缺乏、COVID-19 诊断程序不当以及医疗用品不足都是造成这些毁灭性生命损失的原因。深度学习 (DL) 可用于检测和分类 COVID-19，以进行潜在的基于图像的诊断。材料和方法。本文提出了一种改进的深度卷积神经网络 (IDConv-Net)，用于使用 X 射线和计算机断层扫描 (CT) 图像来检测和分类 COVID-19。在训练阶段之前，将过滤、数据归一化、分类变量编码和数据增强等预处理方法与所提出的 IDConv-Net 结合使用，以提高检测和分类过程的有效性。为了提取基本特征，然后使用深度 CNN。因此，建议的模型可以识别对图像分类任务至关重要的模式和关系，从而获得更精确和有用的诊断。使用 Python 和 Keras（以 TensorFlow 作为后端）进行实验。结果。所提出的 IDConv-Net 使用从巴西圣保罗医院收集的胸部 X 光和 CT 图像以及在线数据库进行了测试。对模型进行评估后，所提出的 IDConv-Net 在 CT 图像的训练和测试中的准确率分别为 99.53% 和 98.41%，在 X 射线图像的训练和测试中分别达到 97.49% 和 96.99% 的准确率。此外，X射线和CT图像的曲线下面积（AUC）值分别为0.954和0.996，表明该模型具有优异的性能。结论。我们提出的 IDConv-Net 模型的研究结果证实，该模型的性能优于现有的 COVID-19 检测和分类模型。IDConv-Net 在 X 射线方面比当前最先进的模型高 2.25%，在 CT 图像方面比当前最先进的模型高 2.81%。此外，IDConv-Net 训练方法比当前的迁移学习模型要快得多。