Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice
<p dir="ltr">Smart health is one of the most popular and important components of smart cities. It is a relatively new context-aware healthcare paradigm influenced by several fields of expertise, such as medical informatics, communications and electronics, bioengineering, ethics, to n...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , |
| منشور في: |
2020
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513505603878912 |
|---|---|
| author | Turker Tuncer (16677966) |
| author2 | Sengul Dogan (16677969) Fatih Ozyurt (4808505) Samir Brahim Belhaouari (9427347) Halima Bensmail (10400) |
| author2_role | author author author author |
| author_facet | Turker Tuncer (16677966) Sengul Dogan (16677969) Fatih Ozyurt (4808505) Samir Brahim Belhaouari (9427347) Halima Bensmail (10400) |
| author_role | author |
| dc.creator.none.fl_str_mv | Turker Tuncer (16677966) Sengul Dogan (16677969) Fatih Ozyurt (4808505) Samir Brahim Belhaouari (9427347) Halima Bensmail (10400) |
| dc.date.none.fl_str_mv | 2020-05-05T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2020.2992641 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Novel_Multi_Center_and_Threshold_Ternary_Pattern_Based_Method_for_Disease_Detection_Method_Using_Voice/27021379 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Health services and systems Information and computing sciences Machine learning MCMTTP discrete wavelet transform voice disease detection smart health machine learning Feature extraction Diseases Medical diagnostic imaging Machine learning Histograms Biomedical engineering Smart healthcare |
| dc.title.none.fl_str_mv | Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Smart health is one of the most popular and important components of smart cities. It is a relatively new context-aware healthcare paradigm influenced by several fields of expertise, such as medical informatics, communications and electronics, bioengineering, ethics, to name a few. Smart health is used to improve healthcare by providing many services such as patient monitoring, early diagnosis of disease and so on. The artificial neural network (ANN), support vector machine (SVM) and deep learning models, especially the convolutional neural network (CNN), are the most commonly used machine learning approaches where they proved to be performance in most cases. Voice disorders are rapidly spreading especially with the development of medical diagnostic systems, although they are often underestimated. Smart health systems can be an easy and fast support to voice pathology detection. The identification of an algorithm that discriminates between pathological and healthy voices with more accuracy is needed to obtain a smart and precise mobile health system. The main contribution of this paper consists of proposing a multiclass-pathologic voice classification using a novel multileveled textural feature extraction with iterative feature selector. Our approach is a simple and efficient voice-based algorithm in which a multi-center and multi threshold based ternary pattern is used (MCMTTP). A more compact multileveled features are then obtained by sample-based discretization techniques and Neighborhood Component Analysis (NCA) is applied to select features iteratively. These features are finally integrated with MCMTTP to achieve an accurate voice-based features detection. Experimental results of six classifiers with three diagnostic diseases (frontal resection, cordectomy and spastic dysphonia) show that the fused features are more suitable for describing voice-based disease detection.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.2992641" target="_blank">https://dx.doi.org/10.1109/access.2020.2992641</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_aa222a8666ce735f0afcb978dccb75f3 |
| identifier_str_mv | 10.1109/access.2020.2992641 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27021379 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using VoiceTurker Tuncer (16677966)Sengul Dogan (16677969)Fatih Ozyurt (4808505)Samir Brahim Belhaouari (9427347)Halima Bensmail (10400)Health sciencesHealth services and systemsInformation and computing sciencesMachine learningMCMTTPdiscrete wavelet transformvoice disease detectionsmart healthmachine learningFeature extractionDiseasesMedical diagnostic imagingMachine learningHistogramsBiomedical engineeringSmart healthcare<p dir="ltr">Smart health is one of the most popular and important components of smart cities. It is a relatively new context-aware healthcare paradigm influenced by several fields of expertise, such as medical informatics, communications and electronics, bioengineering, ethics, to name a few. Smart health is used to improve healthcare by providing many services such as patient monitoring, early diagnosis of disease and so on. The artificial neural network (ANN), support vector machine (SVM) and deep learning models, especially the convolutional neural network (CNN), are the most commonly used machine learning approaches where they proved to be performance in most cases. Voice disorders are rapidly spreading especially with the development of medical diagnostic systems, although they are often underestimated. Smart health systems can be an easy and fast support to voice pathology detection. The identification of an algorithm that discriminates between pathological and healthy voices with more accuracy is needed to obtain a smart and precise mobile health system. The main contribution of this paper consists of proposing a multiclass-pathologic voice classification using a novel multileveled textural feature extraction with iterative feature selector. Our approach is a simple and efficient voice-based algorithm in which a multi-center and multi threshold based ternary pattern is used (MCMTTP). A more compact multileveled features are then obtained by sample-based discretization techniques and Neighborhood Component Analysis (NCA) is applied to select features iteratively. These features are finally integrated with MCMTTP to achieve an accurate voice-based features detection. Experimental results of six classifiers with three diagnostic diseases (frontal resection, cordectomy and spastic dysphonia) show that the fused features are more suitable for describing voice-based disease detection.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.2992641" target="_blank">https://dx.doi.org/10.1109/access.2020.2992641</a></p>2020-05-05T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2020.2992641https://figshare.com/articles/journal_contribution/Novel_Multi_Center_and_Threshold_Ternary_Pattern_Based_Method_for_Disease_Detection_Method_Using_Voice/27021379CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/270213792020-05-05T06:00:00Z |
| spellingShingle | Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice Turker Tuncer (16677966) Health sciences Health services and systems Information and computing sciences Machine learning MCMTTP discrete wavelet transform voice disease detection smart health machine learning Feature extraction Diseases Medical diagnostic imaging Machine learning Histograms Biomedical engineering Smart healthcare |
| status_str | publishedVersion |
| title | Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice |
| title_full | Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice |
| title_fullStr | Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice |
| title_full_unstemmed | Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice |
| title_short | Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice |
| title_sort | Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice |
| topic | Health sciences Health services and systems Information and computing sciences Machine learning MCMTTP discrete wavelet transform voice disease detection smart health machine learning Feature extraction Diseases Medical diagnostic imaging Machine learning Histograms Biomedical engineering Smart healthcare |