A deep learning semantic segmentation architecture for COVID‐19 lesions discovery in limited chest CT datasets

• Published in: Expert systems Vol. 39; no. 6; pp. e12742 - n/a
• Main Authors: Khalifa, Nour Eldeen M., Manogaran, Gunasekaran, Taha, Mohamed Hamed N., Loey, Mohamed
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.07.2022; John Wiley and Sons Inc
• Subjects: Chest; Coders; Computed tomography; COVID-19; CT images; Datasets; Deep learning; Image segmentation; Lesions; Medical imaging; Original; Performance measurement; Semantic segmentation; Semantics; Training; transfer learning; Viral diseases
• DOI: 10.1111/exsy.12742
• ISSN: 0266-4720; 1468-0394

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.