XCovNet: An optimized xception convolutional neural network for classification of COVID-19 from point-of-care lung ultrasound images

• Published in: Multimedia tools and applications Vol. 83; no. 11; pp. 33653 - 33674
• Main Authors: Madhu, G., Kautish, Sandeep, Gupta, Yogita, Nagachandrika, G., Biju, Soly Mathew, Kumar, Manoj
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-03-2024; Springer Nature B.V
• Subjects: Artificial neural networks; Computer Communication Networks; Computer Science; Coronaviruses; COVID-19; Data Structures and Information Theory; Diagnosis; Disease transmission; Feature extraction; Image classification; Machine learning; Medical imaging; Multimedia Information Systems; Neural networks; Parameter identification; Point of care testing; Respiratory system; Special Purpose and Application-Based Systems; Track 2: Medical Applications of Multimedia; Ultrasonic imaging; Viral diseases; COVID-19; Xception; Ultrasound Images; Separable Convolution; Depth-wise Convolution; POCUS
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-16944-z

Global livelihoods are impacted by the novel coronavirus (COVID-19) disease, which mostly affects the respiratory system and spreads via airborne transmission. The disease has spread to almost every nation and is still widespread worldwide. Early and reliable diagnosis is essential to prevent the development of this highly risky disease. The computer-aided diagnostic model facilitates medical practitioners in obtaining a quick and accurate diagnosis. To address these limitations, this study develops an optimized Xception convolutional neural network, called "XCovNet," for recognizing COVID-19 from point-of-care ultrasound (POCUS) images. This model employs a stack of modules, each of which has a slew of feature extractors that enable it to learn richer representations with fewer parameters. The model identifies the presence of COVID-19 by classifying POCUS images containing Coronavirus samples, viral pneumonia samples, and healthy ultrasound images. We compare and evaluate the proposed network with state-of-the-art (SOTA) deep learning models such as VGG, DenseNet, Inception-V3, ResNet, and Xception Networks. By using the XCovNet model, the previous study's problems are cautiously addressed and overhauled by achieving 99.76% accuracy, 99.89% specificity, 99.87% sensitivity, and 99.75% F1-score. To understand the underlying behavior of the proposed network, different tests are performed on different shuffle patterns. Thus, the proposed "XCovNet" can, in regions where test kits are limited, be used to help radiologists detect COVID-19 patients through ultrasound images in the current COVID-19 situation.