Abstract
Numerous deaths have already occurred as a direct result of poor diagnosis procedures and COVID-19 diagnosis errors. The current work suggests deep learning (DL)-based classification approaches for recognising COVID-19 using patient chest X-rays (CHI). It is intended to utilise a deep learning (DL) assist computer-aided finding structure to divide X-ray descriptions into two groups: negative (0) and positive (1), in order to automatically discover COVID-19. The proposed study includes all three processes, including pre-processing, data collecting, and categorization. During pre-processing, distracting noise at the margins is removed. For feature extraction, U-NET-based models and Gaussian blur are used. On the collected features, two DL (DL)-based classifiers are applied: ResNet50 and Inception V3. The SARS-CoV-2 CT-scan (CTS) dataset is utilised to develop and assess COVID classification models. The proposed method was evaluated using a number of outcomes measuring criteria, including precision, recall, accuracy, and F-score. It was demonstrated that when U-NET-based segmentation and Gaussian blur were combined as features, the ResNet50 classifier outperformed the InceptionV3 classifier in terms of performance. The proposed system, using the ResNet50 classifier, surpassed the state-of-the-art COVID-19 classification on the test dataset, achieving 99% accuracy, 94% precision, 96% recall, and 95% F-score.
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Joshi, D., Patel, R., Joshi, A., Maretha, D. (2023). COVID-19 Disease Classification Using DL Architectures. In: Sharma, S., Subudhi, B., Sahu, U.K. (eds) Intelligent Control, Robotics, and Industrial Automation. RCAAI 2022. Lecture Notes in Electrical Engineering, vol 1066. Springer, Singapore. https://doi.org/10.1007/978-981-99-4634-1_74
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