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• W4366376105 abstract "The examination of medical images has benefited greatly from the use of artificial intelligence. In contrast to deep learning systems, which do feature extraction automatically and without human interaction, traditional computer vision methods rely on manually produced features that are particular to a certain domain. Having access to medical information for automated analysis is another major factor driving the trend towards deep learning. Chest x-ray pictures are processed in order to segment the lungs and identify diseases in this thesis. Due to its cheap cost, ease of capture, and non-invasive nature, chest x-ray is the most often used medical imaging technology. However, automatic diagnosis in chest x-rays is difficult due to (1) the presence of the rib-cage and clavicle bones, which can obscure abnormalities that are located beneath them, and (2) the fuzzy intensity transitions near the lung and heart, dense abnormalities, rib-cages, and clavicle bones, which make the identification of lung contours subtle. In x-ray image processing, the Convolutional Neural Network (CNN) is the most often used deep learning architecture. Because to the enormous number of parameters in deep CNN architectures, intensive computing resources are required to train these models. Additionally, chest x-ray datasets are often rather tiny, and there is always the risk of overfitting when developing a model. In this dissertation, we propose five convolutional neural networks (CNNs) to identify illness and segment the lungs in chest x-rays. New Line, New Line In the first research paper, an adaptive lightweight convolutional neural network (ALCNN) is created to detect pneumothorax with few parameters. The model readjusts the feature calibration channel-wise using the convolutional layer and attention mechanism. The suggested model outperformed state-of-the-art deep models trained using three different transfer learning methods. One notable aspect of the suggested model is that it requires ten times less parameters than the best deep models currently available. The second paper suggests the FocusCovid methodology for identifying COVID-19." @default.
• W4366376105 created "2023-04-21" @default.
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• W4366376105 date "2023-02-24" @default.
• W4366376105 modified "2023-09-25" @default.
• W4366376105 title "Covid Analysis using Recurrent Neural Network" @default.
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• W4366376105 doi "https://doi.org/10.1109/icicacs57338.2023.10100129" @default.
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