Topological Evolution of Spiking Neural Networks

Slade, Sam; Zhang, Li

doi:10.1109/ijcnn.2018.8489375

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…Furthermore, the aforementioned A-BiLSTM architecture implemented in this research was shown to be highly effective, but with further experimentation with different layer and hyperparameter settings [24][25][26][27][28][29][30][31][32], additional improvements in performance could be made. Evolutionary algorithms [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49] could also be exploited pertaining to the above parameter tuning as well as architecture generation processes. Moreover, it would also be beneficial to employ additional medical audio datasets to further evaluate model efficiency.…”

Section: Discussionmentioning

confidence: 99%

Deep Recurrent Neural Networks with Attention Mechanisms for Respiratory Anomaly Classification

Wall

Zhang

et al. 2021

2021 International Joint Conference on Neural Networks (IJCNN)

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In recent years, a variety of deep learning techniques and methods have been adopted to provide AI solutions to issues within the medical field, with one specific area being audio-based classification of medical datasets. This research aims to create a novel deep learning architecture for this purpose, with a variety of different layer structures implemented for undertaking audio classification. Specifically, bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Units (GRU) networks in conjunction with an attention mechanism, are implemented in this research for chronic and non-chronic lung disease and COVID-19 diagnosis. We employ two audio datasets, i.e. the Respiratory Sound and the Coswara datasets, to evaluate the proposed model architectures pertaining to lung disease classification. The Respiratory Sound Database contains audio data with respect to lung conditions such as Chronic Obstructive Pulmonary Disease (COPD) and asthma, while the Coswara dataset contains coughing audio samples associated with COVID-19. After a comprehensive evaluation and experimentation process, as the most performant architecture, the proposed attention BiLSTM network (A-BiLSTM) achieves accuracy rates of 96.2% and 96.8% for the Respiratory Sound and the Coswara datasets, respectively. Our research indicates that the implementation of the BiLSTM and attention mechanism was effective in improving performance for undertaking audio classification with respect to various lung condition diagnoses.

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