Deep Learning on Computerized Analysis of Chronic Obstructive Pulmonary Disease
| Autor: | Novruz Allahverdi, Yakup Kutlu, Gokhan Altan |
|---|---|
| Přispěvatelé: | Mühendislik ve Doğa Bilimleri Fakültesi -- Bilgisayar Mühendisliği Bölümü, Altan, Gökhan, Kutlu, Yakup |
| Rok vydání: | 2020 |
| Předmět: |
Clinical assessment
Diseases Receiver operating characteristic 02 engineering and technology Feature learning (machine learning) Deep belief network 0302 clinical medicine Health Information Management Diagnosis Underwater acoustics Chronic obstructive lung disease Frequency modulation 0202 electrical engineering electronic engineering information engineering Interdisciplinary Applications Respiratory tract disease assessment Supervised machine learning Stage (cooking) Empirical mode decomposition Lung RespiratoryDatabase@TR Accuracy COPD Classification (of information) Chronic obstructive pulmonary disease Diagnostic test accuracy study Transforms Classification Respiratory Sounds | Auscultation | Stethoscopes Computer Science Applications Classification parameters Sensitivity and specificity medicine.anatomical_structure Feature (computer vision) Feature extraction 020201 artificial intelligence & image processing Airway obstruction Deep belief networks Information Systems Computerized analysis Human Biotechnology Signal processing Classification performance Unsupervised training Supervised trainings Predictive value Learning algorithms Statistical features False positive result 03 medical and health sciences Pulmonary diseases medicine Training Electrical and Electronic Engineering Classification algorithms Learning systems Hilbert Huang transforms business.industry Deep learning Multichannel lung sound Pattern recognition medicine.disease Biological organs Lung function test respiratory tract diseases Statistical classification Computer Science Mathematical & Computational Biology Artificial intelligence business Controlled study Medical Informatics 030217 neurology & neurosurgery Process optimization |
| Zdroj: | IEEE Journal of Biomedical and Health Informatics. 24:1344-1350 |
| ISSN: | 2168-2208 2168-2194 |
| DOI: | 10.1109/jbhi.2019.2931395 |
| Popis: | Goal: Chronic obstructive pulmonary disease (COPD) is one of the deadliest diseases in the world. Because COPD is an incurable disease and requires considerable time to be diagnosed even by an experienced specialist, it becomes important to provide analysis abnormalities in simple ways. The aim of the study is to compare multiple machine-learning algorithms for the early diagnosis of COPD using multichannel lung sounds. Methods: Deep learning (DL) is an efficient machine-learning algorithm, which comprises unsupervised training to reduce optimization and supervised training by a feature-based distribution of classification parameters. This study focuses on analyzing multichannel lung sounds using statistical features of frequency modulations that are extracted using the Hilbert–Huang transform. Results: Deep-learning algorithm was used in the classification stage of the proposed model to separate the patients with COPD and healthy subjects. The proposed DL model with the Hilbert–Huang transform based statistical features was successful in achieving high classification performance rates of 93.67%, 91%, and 96.33% for accuracy, sensitivity, and specificity, respectively. Conclusion: The proposed computerized analysis of the multichannel lung sounds using DL algorithms provides a standardized assessment with high classification performance. Significance: Our study is a pioneer study that directly focuses on the lung sounds to separate COPD and non-COPD patients. Analyzing 12-channel lung sounds gives the advantages of assessing the entire lung obstructions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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