During the epidemic of COVID-19, Computed Tomography (CT) is used to help in the diagnosis of patients. Most current studies on this subject appear to be focused on broad and private annotated data which are impractical to access from an organization, particularly while radiologists are fighting the coronavirus disease. It is challenging to equate these techniques since they were built on separate datasets, educated on various training sets, and tested using different metrics. In this research, a deep learning semantic segmentation architecture for COVID-19 lesions detection in limited chest CT datasets will be presented. The proposed model architecture consists of the encoder and the decoder components. The encoder component contains three layers of convolution and pooling, while the decoder contains three layers of deconvolutional and upsampling. The dataset consists of 20 CT scans of lungs belongs to 20 patients from two sources of data. The total number of images in the dataset is 3520 CT scans with its labelled images. The dataset is split into 70% for the training phase and 30% for the testing phase. Images of the dataset are passed through the pre-processing phase to be resized and normalized. Five experimental trials are conducted through the research with different images selected for the training and the testing phases for every trial. The proposed model achieves 0.993 in the global accuracy, and 0.987, 0.799, 0.874 for weighted IoU, mean IoU and mean BF score accordingly. The performance metrics such as precision, sensitivity, specificity and F1 score strengthens the obtained results. The proposed model outperforms the related works which use the same dataset in terms of performance and IoU metrics.

中文翻译：

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用于在有限的胸部 CT 数据集中发现 COVID-19 病变的深度学习语义分割架构

在 COVID-19 流行期间，计算机断层扫描 (CT) 用于帮助诊断患者。目前关于这一主题的大多数研究似乎都集中在广泛和私人的注释数据上，这些数据从组织获取是不切实际的，特别是在放射科医生正在与冠状病毒疾病作斗争时。将这些技术等同起来具有挑战性，因为它们建立在单独的数据集上，在不同的训练集上接受教育，并使用不同的指标进行测试。在这项研究中，将介绍一种深度学习语义分割架构，用于在有限的胸部 CT 数据集中检测 COVID-19 病变。所提出的模型架构由编码器和解码器组件组成。编码器组件包含三层卷积和池化，而解码器包含三层反卷积和上采样。该数据集由来自两个数据源的 20 名患者的 20 次肺部 CT 扫描组成。数据集中的图像总数为 3520 次 CT 扫描及其标记图像。数据集分为 70% 用于训练阶段和 30% 用于测试阶段。数据集的图像通过预处理阶段进行调整大小和规范化。通过研究进行了五次实验试验，每次试验选择不同的图像用于训练和测试阶段。所提出的模型在全局精度上达到 0.993，加权 IoU、平均 IoU 和平均 BF 得分分别达到 0.987、0.799、0.874。精度、灵敏度、特异性和 F1 分数等性能指标强化了获得的结果。