COVID-19 Semantic Pneumonia Segmentation and Classification Using Artificial Intelligence. (15th September 2022)

• Record Type: Journal Article
• Authors: Abdulaal, Mohammed J.; Mehedi, Ibrahim M.; Abusorrah, Abdullah M.; Aljohani, Abdulah Jeza; Milyani, Ahmad H.; Rana, Md. Masud; Mahmoud, Mohamed
• Other Names: Tiwari Shailendra Academic Editor.
• Is Part Of: Contrast media & molecular imaging. Volume 2022(2022)
• Publication Date: 2022-09-15
• Subjects: Diagnostic imaging -- Periodicals; Magnetic resonance imaging -- Periodicals; Contrast media (Diagnostic imaging) -- Periodicals; Contrast Media -- Periodicals; Diagnostic Imaging -- Periodicals; Substances de contraste -- Périodiques; Diagnostics moléculaires -- Périodiques; Imagerie médicale; Substance de contraste; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.0754
• DOI: 10.1155/2022/5297709
• Languages: English
• ISSNs: 1555-4309

Abstract

Abstract : Coronavirus 2019 (COVID-19) has become a pandemic. The seriousness of COVID-19 can be realized from the number of victims worldwide and large number of deaths. This paper presents an efficient deep semantic segmentation network (DeepLabv3Plus). Initially, the dynamic adaptive histogram equalization is utilized to enhance the images. Data augmentation techniques are then used to augment the enhanced images. The second stage builds a custom convolutional neural network model using several pretrained ImageNet models and compares them to repeatedly trim the best-performing models to reduce complexity and improve memory efficiency. Several experiments were done using different techniques and parameters. Furthermore, the proposed model achieved an average accuracy of 99.6% and an area under the curve of 0.996 in the COVID-19 detection. This paper will discuss how to train a customized smart convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.