Deep Learning Techniques for Detection of COPD - Vijarania, Meenu; Gupta, Swati
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Deep COPD, an innovative deep learning approach for accurate detection of Chronic Obstructive Pulmonary Disease (COPD) using respiratory sound analysis. The proposed approach utilizes a Convolutional Neural Network (CNN) model trained on a respiratory sound database containing wheezes, crackles, and both crackles and wheezes. To overcome the challenge of a small dataset, innovative techniques such as device-specific fine-tuning, concatenation-based augmentation, blank region clipping, and smart padding are employed. These techniques enable efficient utilization of the dataset, resulting in an impressive accuracy of 90% to 95%.…mehr

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Produktbeschreibung
Deep COPD, an innovative deep learning approach for accurate detection of Chronic Obstructive Pulmonary Disease (COPD) using respiratory sound analysis. The proposed approach utilizes a Convolutional Neural Network (CNN) model trained on a respiratory sound database containing wheezes, crackles, and both crackles and wheezes. To overcome the challenge of a small dataset, innovative techniques such as device-specific fine-tuning, concatenation-based augmentation, blank region clipping, and smart padding are employed. These techniques enable efficient utilization of the dataset, resulting in an impressive accuracy of 90% to 95%.
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Autorenporträt
Dr Meenu VijaraniaAssociate Professor CSE DepartmentK.R Mangalam University Gurgaon.Dr Swati GuptaAssociate Professor CSE DepartmentK.R Mangalam University Gurgaon.