Inception-Based Network and Multi-Spectrogram Ensemble Applied To Predict Respiratory Anomalies and Lung Diseases

Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases

“…As the potential results were achieved from our previous work [7], we further evaluate different inception based network architectures in this paper. In particular, two high-level architectures with single or double inception layers as shown in Table I are used in this paper.…”

“…As proposed deep learning frameworks are shown in Figure 1, we firstly duplicate the short-time cycles or cut off the long-time cycles to make all respiratory cycles equal to 10 seconds. For the first two deep learning frameworks (I) and (II), we extract Wavelet-based spectrograms which proves effective in our previous work [7]. As we reuse the setting from [7], we then generate Wavelet spectrograms of 124×154 from 10-second respiratory cycles.…”

“…For the first two deep learning frameworks (I) and (II), we extract Wavelet-based spectrograms which proves effective in our previous work [7]. As we reuse the setting from [7], we then generate Wavelet spectrograms of 124×154 from 10-second respiratory cycles. Meanwhile, we extract log-Mel spectrogram in the deep learning framework (III) as we re-use pre-trained models from [14] receiving log-Mel spectrogram input.…”

“…Meanwhile, deep learning based systems make use of spectrogram where both temporal and spectral features are well represented. These spectrograms are then explored by a wide range of convolutional deep neural networks (CNNs) [4], [5], [6], [7] or recurrent neural networks (RNNs) [8], [9]. Compare between two approaches, deep learning based systems are more effective and show potential results mentioned in [4], [6], [7].…”

An Ensemble of Deep Learning Frameworks for Predicting Respiratory Anomalies

“…Meanwhile, deep learning based systems make use of raw inputs such as waveforms or spectrograms, with a trained feature extractor. Spectrograms, in which both temporal and spectral feature elements are well represented, have been explored by a wide range of deep and convolutional neural networks (CNNs) [4], [5], [6], [7] and recurrent neural networks (RNNs) [8]. Comparing between machine learning approaches with hand-crafted features, and deep learning systems with trained feature extractors, the latter are widely reported as being more effective for respiratory classification tasks [4], [6], [7].…”