ArticlePeerJ. Computer science2021
ADID-UNET-a segmentation model for COVID-19 infection from lung CT scans.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 62 citations in OpenAlex.
- Encoder-decoder convolutional neural network for simple CT segmentation of COVID-19 infected lungs.PeerJ. Computer science · 2024Article
- Enhancing COVID-19 CT Image Segmentation: A Comparative Study of Attention and Recurrence in UNet Models.Journal of imaging · 2023Article
- [Corona virus disease 2019 lesion segmentation network based on an adaptive joint loss function].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- ADU-Net: An Attention Dense U-Net based deep supervised DNN for automated lesion segmentation of COVID-19 from chest CT images.Biomedical signal processing and control · 2023Article
- SAA-UNet: Spatial Attention and Attention Gate UNet for COVID-19 Pneumonia Segmentation from Computed Tomography.Diagnostics (Basel, Switzerland) · 2023Article
- Deep-Learning-Based Automatic Segmentation of Parotid Gland on Computed Tomography Images.Diagnostics (Basel, Switzerland) · 2023Article
- COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled.Archives of computational methods in engineering : state of the art reviews · 2023Review
- Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust.PloS one · 2022Article
- Ultrasound image denoising using generative adversarial networks with residual dense connectivity and weighted joint loss.PeerJ. Computer science · 2022Article
- Automatic COVID-19 Lung Infection Segmentation through Modified Unet Model.Journal of healthcare engineering · 2022Article
- Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation.Sustainable cities and society · 2021Article
- Decoders configurations based on Unet family and feature pyramid network for COVID-19 segmentation on CT images.PeerJ. Computer science · 2021Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Currently, the new coronavirus disease (COVID-19) is one of the biggest health crises threatening the world. Automatic detection from computed tomography (CT) scans is a classic method to detect lung infection, but it faces problems such as high variations in intensity, indistinct edges near lung infected region and noise due to data acquisition process. Therefore, this article proposes a new COVID-19 pulmonary infection segmentation depth network referred as the Attention Gate-Dense Network- Improved Dilation Convolution-UNET (ADID-UNET). The dense network replaces convolution and maximum pooling function to enhance feature propagation and solves gradient disappearance problem. An improved dilation convolution is used to increase the receptive field of the encoder output to further obtain more edge features from the small infected regions. The integration of attention gate into the model suppresses the background and improves prediction accuracy. The experimental results show that the ADID-UNET model can accurately segment COVID-19 lung infected areas, with performance measures greater than 80% for metrics like Accuracy, Specificity and Dice Coefficient (DC). Further when compared to other state-of-the-art architectures, the proposed model showed excellent segmentation effects with a high DC and F1 score of 0.8031 and 0.82 respectively.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.