دورية أكاديمية
LW‐CovidNet: Automatic covid‐19 lung infection detection from chest X‐ray images
| العنوان: | LW‐CovidNet: Automatic covid‐19 lung infection detection from chest X‐ray images |
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| المؤلفون: | Noor Ahmed, Xin Tan, Lizhuang Ma |
| المصدر: | IET Image Processing, Vol 17, Iss 2, Pp 362-374 (2023) |
| بيانات النشر: | Wiley, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Computer software |
| مصطلحات موضوعية: | Photography, TR1-1050, Computer software, QA76.75-76.765 |
| الوصف: | Abstract Coronavirus Disease 2019 (Covid‐19) overtook the worldwide in early 2020, placing the world's health in threat. Automated lung infection detection using Chest X‐ray images has a ton of potential for enhancing the traditional covid‐19 treatment strategy. However, there are several challenges to detect infected regions from Chest X‐ray images, including significant variance in infected features similar spatial characteristics, multi‐scale variations in texture shapes and sizes of infected regions. Moreover, high parameters with transfer learning are also a constraints to deploy deep convolutional neural network(CNN) models in real time environment. A novel covid‐19 lightweight CNN(LW‐CovidNet) method is proposed to automatically detect covid‐19 infected regions from Chest X‐ray images to address these challenges. In our proposed hybrid method of integrating Standard and Depth‐wise Separable convolutions are used to aggregate the high level features and also compensate the information loss by increasing the Receptive Field of the model. The detection boundaries of disease regions representations are then enhanced via an Edge‐Attention method by applying heatmaps for accurate detection of disease regions. Extensive experiments indicate that the proposed LW‐CovidNet surpasses most cutting‐edge detection methods and also contributes to the advancement of state‐of‐the‐art performance. It is envisaged that with reliable accuracy, this method can be introduced for clinical practices in the future. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1751-9667 1751-9659 |
| Relation: | https://doaj.org/toc/1751-9659; https://doaj.org/toc/1751-9667 |
| DOI: | 10.1049/ipr2.12637 |
| URL الوصول: | https://doaj.org/article/462945870c7b4f0abd6a2f9c3315415f |
| رقم الأكسشن: | edsdoj.462945870c7b4f0abd6a2f9c3315415f |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 17519667 17519659 |
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| DOI: | 10.1049/ipr2.12637 |