A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method
Abstract In the healthcare domain, the essential task is to understand and classify diseases affecting the vocal folds (VFs). The accurate identification of VF disease is the key issue in this domain. Integrating VF segmentation and disease classification into a single system is challenging but impo...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-64987-5 |
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| author | S. M. Nuruzzaman Nobel S. M. Masfequier Rahman Swapno Md. Rajibul Islam Mejdl Safran Sultan Alfarhood M. F. Mridha |
| author_facet | S. M. Nuruzzaman Nobel S. M. Masfequier Rahman Swapno Md. Rajibul Islam Mejdl Safran Sultan Alfarhood M. F. Mridha |
| author_sort | S. M. Nuruzzaman Nobel |
| collection | DOAJ |
| description | Abstract In the healthcare domain, the essential task is to understand and classify diseases affecting the vocal folds (VFs). The accurate identification of VF disease is the key issue in this domain. Integrating VF segmentation and disease classification into a single system is challenging but important for precise diagnostics. Our study addresses this challenge by combining VF illness categorization and VF segmentation into a single integrated system. We utilized two effective ensemble machine learning methods: ensemble EfficientNetV2L-LGBM and ensemble UNet-BiGRU. We utilized the EfficientNetV2L-LGBM model for classification, achieving a training accuracy of 98.88%, validation accuracy of 97.73%, and test accuracy of 97.88%. These exceptional outcomes highlight the system’s ability to classify different VF illnesses precisely. In addition, we utilized the UNet-BiGRU model for segmentation, which attained a training accuracy of 92.55%, a validation accuracy of 89.87%, and a significant test accuracy of 91.47%. In the segmentation task, we examined some methods to improve our ability to divide data into segments, resulting in a testing accuracy score of 91.99% and an Intersection over Union (IOU) of 87.46%. These measures demonstrate skill of the model in accurately defining and separating VF. Our system’s classification and segmentation results confirm its capacity to effectively identify and segment VF disorders, representing a significant advancement in enhancing diagnostic accuracy and healthcare in this specialized field. This study emphasizes the potential of machine learning to transform the medical field’s capacity to categorize VF and segment VF, providing clinicians with a vital instrument to mitigate the profound impact of the condition. Implementing this innovative approach is expected to enhance medical procedures and provide a sense of optimism to those globally affected by VF disease. |
| format | Article |
| id | doaj-art-65895489835849d6bcbd3eee3f9181cd |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-65895489835849d6bcbd3eee3f9181cd2025-01-26T12:34:54ZengNature PortfolioScientific Reports2045-23222024-06-0114112510.1038/s41598-024-64987-5A machine learning approach for vocal fold segmentation and disorder classification based on ensemble methodS. M. Nuruzzaman Nobel0S. M. Masfequier Rahman Swapno1Md. Rajibul Islam2Mejdl Safran3Sultan Alfarhood4M. F. Mridha5Department of Computer Science and Engineering, Bangladesh University of Business and TechnologyDepartment of Computer Science and Engineering, Bangladesh University of Business and TechnologyDepartment of Electrical and Electronic Engineering, The Hong Kong Polytechnic UniversityDepartment of Computer Science, College of Computer and Information Sciences, King Saud UniversityDepartment of Computer Science, College of Computer and Information Sciences, King Saud UniversityDepartment of Computer Science, American International University-BangladeshAbstract In the healthcare domain, the essential task is to understand and classify diseases affecting the vocal folds (VFs). The accurate identification of VF disease is the key issue in this domain. Integrating VF segmentation and disease classification into a single system is challenging but important for precise diagnostics. Our study addresses this challenge by combining VF illness categorization and VF segmentation into a single integrated system. We utilized two effective ensemble machine learning methods: ensemble EfficientNetV2L-LGBM and ensemble UNet-BiGRU. We utilized the EfficientNetV2L-LGBM model for classification, achieving a training accuracy of 98.88%, validation accuracy of 97.73%, and test accuracy of 97.88%. These exceptional outcomes highlight the system’s ability to classify different VF illnesses precisely. In addition, we utilized the UNet-BiGRU model for segmentation, which attained a training accuracy of 92.55%, a validation accuracy of 89.87%, and a significant test accuracy of 91.47%. In the segmentation task, we examined some methods to improve our ability to divide data into segments, resulting in a testing accuracy score of 91.99% and an Intersection over Union (IOU) of 87.46%. These measures demonstrate skill of the model in accurately defining and separating VF. Our system’s classification and segmentation results confirm its capacity to effectively identify and segment VF disorders, representing a significant advancement in enhancing diagnostic accuracy and healthcare in this specialized field. This study emphasizes the potential of machine learning to transform the medical field’s capacity to categorize VF and segment VF, providing clinicians with a vital instrument to mitigate the profound impact of the condition. Implementing this innovative approach is expected to enhance medical procedures and provide a sense of optimism to those globally affected by VF disease.https://doi.org/10.1038/s41598-024-64987-5 |
| spellingShingle | S. M. Nuruzzaman Nobel S. M. Masfequier Rahman Swapno Md. Rajibul Islam Mejdl Safran Sultan Alfarhood M. F. Mridha A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method Scientific Reports |
| title | A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method |
| title_full | A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method |
| title_fullStr | A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method |
| title_full_unstemmed | A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method |
| title_short | A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method |
| title_sort | machine learning approach for vocal fold segmentation and disorder classification based on ensemble method |
| url | https://doi.org/10.1038/s41598-024-64987-5 |
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