Vol.71, No.1, 2022, pp.651-666, doi:10.32604/cmc.2022.020820
OPEN ACCESS
ARTICLE
Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection
  • A. Devipriya1, P. Prabu2, K. Venkatachalam3, Ahmed Zohair Ibrahim4,*
1 Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, 641407, India
2 Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India
3 Department of Applied Cybernetics, Faculty of Science, University of Hradec Králové, 50003, Hradec Králové, Czech Republic
4 Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, KSA.P.o.Box: 84428, Postal Code:11671
* Corresponding Author: Ahmed Zohair Ibrahim. Email:
Received 09 June 2021; Accepted 23 August 2021; Issue published 03 November 2021
Abstract
This research article proposes an automatic frame work for detecting COVID -19 at the early stage using chest X-ray image. It is an undeniable fact that coronovirus is a serious disease but the early detection of the virus present in human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in need of right and even rich technology for its early detection. The proposed deep learning model analysis the pixels of every image and adjudges the presence of virus. The classifier is designed in such a way so that, it automatically detects the virus present in lungs using chest image. This approach uses an image texture analysis technique called granulometric mathematical model. Selected features are heuristically processed for optimization using novel multi scaling deep learning called light weight residual–atrous spatial pyramid pooling (LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPP-Unet technique has a higher level of contracting solution by extracting major level of image features. Moreover, the corona virus has been detected using high resolution output. In the framework, atrous spatial pyramid pooling (ASPP) method is employed at its bottom level for incorporating the deep multi scale features in to the discriminative mode. The architectural working starts from the selecting the features from the image using granulometric mathematical model and the selected features are optimized using LightRES-ASPP-Unet. ASPP in the analysis of images has performed better than the existing Unet model. The proposed algorithm has achieved 99.6% of accuracy in detecting the virus at its early stage.
Keywords
Deep residual learning; convolutional neural network; COVID-19; X-ray; principal component analysis; granulo metrics texture analysis
Cite This Article
Devipriya, A., Prabu, P., Venkatachalam, K., Ibrahim, A. Z. (2022). Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection. CMC-Computers, Materials & Continua, 71(1), 651–666.
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