A review on lung disease recognition by acoustic signal analysis with deep learning networks
Abstract Recently, assistive explanations for difficulties in the health check area have been made viable thanks in considerable portion to technologies like deep learning and machine learning. Using auditory analysis and medical imaging, they also increase the predictive accuracy for prompt and ear...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-06-01
|
| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-023-00762-z |
| _version_ | 1797801377234157568 |
|---|---|
| author | Alyaa Hamel Sfayyih Nasri Sulaiman Ahmad H. Sabry |
| author_facet | Alyaa Hamel Sfayyih Nasri Sulaiman Ahmad H. Sabry |
| author_sort | Alyaa Hamel Sfayyih |
| collection | DOAJ |
| description | Abstract Recently, assistive explanations for difficulties in the health check area have been made viable thanks in considerable portion to technologies like deep learning and machine learning. Using auditory analysis and medical imaging, they also increase the predictive accuracy for prompt and early disease detection. Medical professionals are thankful for such technological support since it helps them manage further patients because of the shortage of skilled human resources. In addition to serious illnesses like lung cancer and respiratory diseases, the plurality of breathing difficulties is gradually rising and endangering society. Because early prediction and immediate treatment are crucial for respiratory disorders, chest X-rays and respiratory sound audio are proving to be quite helpful together. Compared to related review studies on lung disease classification/detection using deep learning algorithms, only two review studies based on signal analysis for lung disease diagnosis have been conducted in 2011 and 2018. This work provides a review of lung disease recognition with acoustic signal analysis with deep learning networks. We anticipate that physicians and researchers working with sound-signal-based machine learning will find this material beneficial. |
| first_indexed | 2024-03-13T04:49:33Z |
| format | Article |
| id | doaj.art-7592a2c4b9fc4f3b97ab5aa44d620162 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-03-13T04:49:33Z |
| publishDate | 2023-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-7592a2c4b9fc4f3b97ab5aa44d6201622023-06-18T11:16:35ZengSpringerOpenJournal of Big Data2196-11152023-06-0110112310.1186/s40537-023-00762-zA review on lung disease recognition by acoustic signal analysis with deep learning networksAlyaa Hamel Sfayyih0Nasri Sulaiman1Ahmad H. Sabry2Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra MalaysiaDepartment of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra MalaysiaDepartment of Computer Engineering, Al-Nahrain UniversityAbstract Recently, assistive explanations for difficulties in the health check area have been made viable thanks in considerable portion to technologies like deep learning and machine learning. Using auditory analysis and medical imaging, they also increase the predictive accuracy for prompt and early disease detection. Medical professionals are thankful for such technological support since it helps them manage further patients because of the shortage of skilled human resources. In addition to serious illnesses like lung cancer and respiratory diseases, the plurality of breathing difficulties is gradually rising and endangering society. Because early prediction and immediate treatment are crucial for respiratory disorders, chest X-rays and respiratory sound audio are proving to be quite helpful together. Compared to related review studies on lung disease classification/detection using deep learning algorithms, only two review studies based on signal analysis for lung disease diagnosis have been conducted in 2011 and 2018. This work provides a review of lung disease recognition with acoustic signal analysis with deep learning networks. We anticipate that physicians and researchers working with sound-signal-based machine learning will find this material beneficial.https://doi.org/10.1186/s40537-023-00762-zDeep learningAudio-based diagnosisLung soundRespiratory systemSignal analysisCNN |
| spellingShingle | Alyaa Hamel Sfayyih Nasri Sulaiman Ahmad H. Sabry A review on lung disease recognition by acoustic signal analysis with deep learning networks Journal of Big Data Deep learning Audio-based diagnosis Lung sound Respiratory system Signal analysis CNN |
| title | A review on lung disease recognition by acoustic signal analysis with deep learning networks |
| title_full | A review on lung disease recognition by acoustic signal analysis with deep learning networks |
| title_fullStr | A review on lung disease recognition by acoustic signal analysis with deep learning networks |
| title_full_unstemmed | A review on lung disease recognition by acoustic signal analysis with deep learning networks |
| title_short | A review on lung disease recognition by acoustic signal analysis with deep learning networks |
| title_sort | review on lung disease recognition by acoustic signal analysis with deep learning networks |
| topic | Deep learning Audio-based diagnosis Lung sound Respiratory system Signal analysis CNN |
| url | https://doi.org/10.1186/s40537-023-00762-z |
| work_keys_str_mv | AT alyaahamelsfayyih areviewonlungdiseaserecognitionbyacousticsignalanalysiswithdeeplearningnetworks AT nasrisulaiman areviewonlungdiseaserecognitionbyacousticsignalanalysiswithdeeplearningnetworks AT ahmadhsabry areviewonlungdiseaserecognitionbyacousticsignalanalysiswithdeeplearningnetworks AT alyaahamelsfayyih reviewonlungdiseaserecognitionbyacousticsignalanalysiswithdeeplearningnetworks AT nasrisulaiman reviewonlungdiseaserecognitionbyacousticsignalanalysiswithdeeplearningnetworks AT ahmadhsabry reviewonlungdiseaserecognitionbyacousticsignalanalysiswithdeeplearningnetworks |