ArticleSN computer science2022
An Encoder-Decoder-Based Method for Segmentation of COVID-19 Lung Infection in CT Images.
Article in SN computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Encoder-decoder convolutional neural network for simple CT segmentation of COVID-19 infected lungs.PeerJ. Computer science · 2024Article
- Remora Namib Beetle Optimization Enabled Deep Learning for Severity of COVID-19 Lung Infection Identification and Classification Using CT Images.Sensors (Basel, Switzerland) · 2023Article
- EffViT-COVID: A dual-path network for COVID-19 percentage estimation.Expert systems with applications · 2023Article
- Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives.Array (New York, N.Y.) · 2023Review
- PDRF-Net: a progressive dense residual fusion network for COVID-19 lung CT image segmentation.Evolving systems · 2023Article
- Deep-Learning-Based Automatic Segmentation of Parotid Gland on Computed Tomography Images.Diagnostics (Basel, Switzerland) · 2023Article
- A Survey on Deep-Learning-Based Diabetic Retinopathy Classification.Diagnostics (Basel, Switzerland) · 2023Review
- Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2022Article
- A fast lightweight network for the discrimination of COVID-19 and pulmonary diseases.Biomedical signal processing and control · 2022Article
- Effective multiscale deep learning model for COVID19 segmentation tasks: A further step towards helping radiologist.Neurocomputing · 2022Article
- BEMD-3DCNN-based method for COVID-19 detection.Computers in biology and medicine · 2022Article
- Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks.Computers in biology and medicine · 2022Review
- COLI-Net: Deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images.International journal of imaging systems and technology · 2022Article
- Unsupervised domain adaptation based COVID-19 CT infection segmentation network.Applied intelligence (Dordrecht, Netherlands) · 2022Article
- COVID-view: Diagnosis of COVID-19 using Chest CT.IEEE transactions on visualization and computer graphics · 2022Article
- FAM: focal attention module for lesion segmentation of COVID-19 CT images.Journal of real-time image processing · 2022Article
- Automatic COVID-19 Lung Infection Segmentation through Modified Unet Model.Journal of healthcare engineering · 2022Article
- Detection and Prevention of Virus Infection.Advances in experimental medicine and biology · 2022Article
- COVID-19 lung infection segmentation with a novel two-stage cross-domain transfer learning framework.Medical image analysis · 2021Article
- Multilevel depth-wise context attention network with atrous mechanism for segmentation of COVID19 affected regions.Neural computing & applications · 2021Article
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3 authors.
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Abstract
The novelty of the COVID-19 Disease and the speed of spread, created colossal chaotic, impulse all the worldwide researchers to exploit all resources and capabilities to understand and analyze characteristics of the coronavirus in terms of spread ways and virus incubation time. For that, the existing medical features such as CT-scan and X-ray images are used. For example, CT-scan images can be used for the detection of lung infection. However, the quality of these images and infection characteristics limit the effectiveness of these features. Using artificial intelligence (AI) tools and computer vision algorithms, the accuracy of detection can be more accurate and can help to overcome these issues. In this paper, we propose a multi-task deep-learning-based method for lung infection segmentation on CT-scan images. Our proposed method starts by segmenting the lung regions that may be infected. Then, segmenting the infections in these regions. In addition, to perform a multi-class segmentation the proposed model is trained using the two-stream inputs. The multi-task learning used in this paper allows us to overcome the shortage of labeled data. In addition, the multi-input stream allows the model to learn from many features that can improve the results. To evaluate the proposed method, many metrics have been used including Sorensen-Dice similarity, Sensitivity, Specificity, Precision, and MAE metrics. As a result of experiments, the proposed method can segment lung infections with high performance even with the shortage of data and labeled images. In addition, comparing with the state-of-the-art method our method achieves good performance results. For example, the proposed method reached 78..6% for Dice, 71.1% for Sensitivity metric, 99.3% for Specificity 85.6% for Precision, and 0.062 for Mean Average Error metric, which demonstrates the effectiveness of the proposed method for lung infection segmentation.
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