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Classification of Lung Images of COVID-19 Patients With the Application of Deep Learning Techniques
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Author(s): C. Meenakshi (Vels Institute of Science, Technology, Advanced Studies, India), S. Meyyappan (MIT Campus, Anna University, Chennai, India), A. Ganesh Ram (Anna University, India), M. Vijayakarthick (Anna University, India), N. Vinoth (Anna University, India)and Bhopendra Singh (Amity University, Dubai, UAE)
Copyright: 2024
Pages: 14
Source title:
Advancements in Clinical Medicine
Source Author(s)/Editor(s): P. Paramasivan (Dhaanish Ahmed College of Engineering, India), S. Suman Rajest (Dhaanish Ahmed College of Engineering, India), Karthikeyan Chinnusamy (Veritas, USA), R. Regin (SRM Instıtute of Science and Technology, India)and Ferdin Joe John Joseph (Thai-Nichi Institute of Technology, Thailand)
DOI: 10.4018/979-8-3693-5946-4.ch005
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Abstract
This study introduces a smart approach that uses deep learning and feature extraction from chest CT scans to detect COVID-19 quickly and accurately. Strategically integrating transfer learning with pre-trained models to improve COVID-19 diagnosis is the major innovation. Two key phases comprise the research approach. Transfer learning is first used to deep learning models using CNNs like MobileNet, DenseNet, Xception, ResNet, InceptionV3, InceptionResNetV2, VGGNet, and NASNet. PCA is used to improve feature representation and classification accuracy in these models after extensive training, testing, and validation. Kapur's entropy thresholding, morphology-based segmentation, and k-means clustering, enriched by transfer learning paradigms, are used for feature extraction. High-quality features are extracted using these methods, improving CT picture interpretability and informativeness. The results reveal that this integrative strategy improves detection accuracy, sensitivity, specificity, and performance.
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