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Hybrid LSTM-KAN for Respiratory Sound Classification
Read Full Article: Hybrid LSTM-KAN for Respiratory Sound Classification
The investigation explores the use of hybrid Long Short-Term Memory (LSTM) and Knowledge Augmented Network (KAN) architectures for classifying respiratory sounds in imbalanced datasets. This approach aims to improve the accuracy and reliability of respiratory sound classification, which is crucial for medical diagnostics. By combining LSTM's ability to handle sequential data with KAN's knowledge integration, the study seeks to address the challenges posed by imbalanced data, potentially leading to better healthcare outcomes. This matters because improving diagnostic tools can lead to more accurate and timely medical interventions.
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