A Deep Ensemble Neural Network with Attention Mechanisms for Lung Abnormality Classification Using Audio Inputs

Wall, Conor; Zhang, Li; Yu, Yonghong; Kumar, Akshi; Gao, Rong

doi:10.3390/s22155566

Cited by 15 publications

(5 citation statements)

References 55 publications

(74 reference statements)

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“…Due to this architecture, they are especially suitable for analyzing sequential events like electrocardiograms, 16 audio data in auscultation, 17 and language processing. 18 Other promising fields are the prediction of in‐hospital cardiac arrest or acute kidney injury in hospitalized patients.…”

Section: Special Ai Algorithms and Their Applicationsmentioning

confidence: 99%

Principles of artificial intelligence and its application in cardiovascular medicine

Wieneke,

Voigt

2023

Clinical Cardiology

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Artificial intelligence (AI) represents a rapidly developing field. Its use can improve diagnosis and therapy in many areas of medicine. Despite this enormous progress, many physicians perceive it as a black box and are skeptical about it. This review will present the basics of machine learning. Different classifications of artificial intelligence, such as supervised versus unsupervised and discriminative versus generative AI, are given. Analogies to human intelligence are discussed as far as algorithms are oriented toward it. In the second step, the most common models like random forest, k‐means clustering, convolutional neural network, and transformers will be presented in a way that the underlying idea can be understood. Corresponding medical applications in cardiovascular medicine will be named for all models, respectively. The overview is intended to show that the term artificial intelligence covers a wide range of different concepts. It should help physicians understand the principles of AI to make up one's minds about its application in cardiology. It should also enable them to evaluate results obtained with AI's help critically.

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Section: Special Ai Algorithms and Their Applicationsmentioning

confidence: 99%

Principles of artificial intelligence and its application in cardiovascular medicine

Wieneke,

Voigt

2023

Clinical Cardiology

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“…The recordings are collected from 126 patients. It contains 6898 respiratory cycles wherein 1864, 886, and 506 contain crackles, wheezes and both crackles and wheeze respectively [40], [41]. • WBCD: The dataset was created at the University of Wisconsin Hospitals in 1992.…”

Section: A Datasetsmentioning

confidence: 99%

Particle Swarm Optimization-Based Random Forest Framework for the Classification of Chronic Diseases

Singh,

Prakash,

Jain

2023

IEEE Access

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In this paper, a hybrid metaheuristic-based Machine learning approach has been propounded for the classification of various Chronic Diseases (CDs). The CDs often get misdiagnosed due to various issues viz., similar and overlapping symptoms, sensitive devices, lack of clinical experts, etc. Based on the above issues, this study has utilized a fusion of Particle Swarm Optimization with Random Forest (PSORF) for the automatic identification of CDs. The approach PSORF comprises of two main components: PSO for obtaining the minimal optimal feature set, also to optimize the performance of the RF classifier, and RF classifier for the classification of multiple CDs. In this research, five different CD datasets have been deployed onto a series of experiments have been conducted to identify the best approach for the classification of CDs. To address the issues of imbalanced and incomplete data in the datasets used, Synthetic Minority Oversampling Technique (SMOTE) and Expected Minimization (EM) Imputation techniques have been applied before training the model. This ensures the data quality is improved before being used for analysis. Furthermore, the performance of the PSO and RF classifiers has been compared with other metaheuristic and ML classifiers in terms of different performance metrics. For this purpose, Friedman's tests have been employed to calculate the mean ranks of all the classifiers across all the datasets for different metrics. The results showed that the proposed technique achieved the highest mean rank in terms of Accuracy, F-measure, and Receiver Operating Characteristics (ROC) across all four datasets.

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“…However, the previous challenge was mitigated in 2019 with the emergence of the largest public database, the International Conference on Biomedical and Health Informatics (ICBHI) [ 60 , 61 ]. Therefore, research focused on different machine learning approaches has recently increased dramatically, such as Recurrent Neural Networks (RNN) [ 62 ], hybrid neural networks [ 63 , 64 , 65 , 66 , 67 ] and above all Convolutional Neural Networks (CNN) [ 64 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 ]. Thus, the use of these types of deep learning architectures provided promising performance improvements due to their ability that they are able to learn behaviour, both in time and frequency, from large datasets, eliminating the engineer intervention in feature extraction techniques, which reduces the likelihood of human error [ 100 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Adventitious Sounds Combining Cochleogram and Vision Transformers

Mang,

González Martínez,

Martinez Muñoz

et al. 2024

Sensors

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Early identification of respiratory irregularities is critical for improving lung health and reducing global mortality rates. The analysis of respiratory sounds plays a significant role in characterizing the respiratory system’s condition and identifying abnormalities. The main contribution of this study is to investigate the performance when the input data, represented by cochleogram, is used to feed the Vision Transformer (ViT) architecture, since this input–classifier combination is the first time it has been applied to adventitious sound classification to our knowledge. Although ViT has shown promising results in audio classification tasks by applying self-attention to spectrogram patches, we extend this approach by applying the cochleogram, which captures specific spectro-temporal features of adventitious sounds. The proposed methodology is evaluated on the ICBHI dataset. We compare the classification performance of ViT with other state-of-the-art CNN approaches using spectrogram, Mel frequency cepstral coefficients, constant-Q transform, and cochleogram as input data. Our results confirm the superior classification performance combining cochleogram and ViT, highlighting the potential of ViT for reliable respiratory sound classification. This study contributes to the ongoing efforts in developing automatic intelligent techniques with the aim to significantly augment the speed and effectiveness of respiratory disease detection, thereby addressing a critical need in the medical field.

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A Deep Ensemble Neural Network with Attention Mechanisms for Lung Abnormality Classification Using Audio Inputs

Cited by 15 publications

References 55 publications

Principles of artificial intelligence and its application in cardiovascular medicine

Principles of artificial intelligence and its application in cardiovascular medicine

Particle Swarm Optimization-Based Random Forest Framework for the Classification of Chronic Diseases

Classification of Adventitious Sounds Combining Cochleogram and Vision Transformers

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