Improving Tuberculosis Diagnosis using Explainable Artificial Intelligence in Medical Imaging
The integration of artificial intelligence (AI) applications in the healthcare sector is ushering in a significant transformation, particularly in developing more effective strategies for early diagnosis and treatment of contagious diseases like tuberculosis. Tuberculosis, a global public health cha...
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| Format: | Article |
| Language: | English |
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Mahmut Akyigit
2024-05-01
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| Series: | Journal of Mathematical Sciences and Modelling |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/3649789 |
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| author | Cem Özkurt |
| author_facet | Cem Özkurt |
| author_sort | Cem Özkurt |
| collection | DOAJ |
| description | The integration of artificial intelligence (AI) applications in the healthcare sector is ushering in a significant transformation, particularly in developing more effective strategies for early diagnosis and treatment of contagious diseases like tuberculosis. Tuberculosis, a global public health challenge, demands swift interventions to prevent its spread. While deep learning and image processing techniques show potential in extracting meaningful insights from complex radiological images, their accuracy is often scrutinized due to a lack of explainability.This research navigates the intersection of AI and tuberculosis diagnosis by focusing on explainable artificial intelligence (XAI). A meticulously designed deep learning model for tuberculosis detection is introduced alongside an exploration of XAI to unravel complex decisions.The core belief is that XAI, by elucidating diagnostic decision rationale, enhances the reliability of AI in clinical settings. Emphasizing the pivotal role of XAI in tuberculosis diagnosis, this study aims to impact future research and practical implementations, fostering the adoption of AI-driven disease diagnosis methodologies for global health improvement. |
| format | Article |
| id | doaj-art-be4ead48f07c41d9b1074a4e6d9a3fab |
| institution | Kabale University |
| issn | 2636-8692 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Mahmut Akyigit |
| record_format | Article |
| series | Journal of Mathematical Sciences and Modelling |
| spelling | doaj-art-be4ead48f07c41d9b1074a4e6d9a3fab2024-12-31T09:54:20ZengMahmut AkyigitJournal of Mathematical Sciences and Modelling2636-86922024-05-0171334410.33187/jmsm.14171601408Improving Tuberculosis Diagnosis using Explainable Artificial Intelligence in Medical ImagingCem Özkurt0https://orcid.org/0000-0002-1251-7715SAKARYA UYGULAMALI BİLİMLER ÜNİVERSİTESİThe integration of artificial intelligence (AI) applications in the healthcare sector is ushering in a significant transformation, particularly in developing more effective strategies for early diagnosis and treatment of contagious diseases like tuberculosis. Tuberculosis, a global public health challenge, demands swift interventions to prevent its spread. While deep learning and image processing techniques show potential in extracting meaningful insights from complex radiological images, their accuracy is often scrutinized due to a lack of explainability.This research navigates the intersection of AI and tuberculosis diagnosis by focusing on explainable artificial intelligence (XAI). A meticulously designed deep learning model for tuberculosis detection is introduced alongside an exploration of XAI to unravel complex decisions.The core belief is that XAI, by elucidating diagnostic decision rationale, enhances the reliability of AI in clinical settings. Emphasizing the pivotal role of XAI in tuberculosis diagnosis, this study aims to impact future research and practical implementations, fostering the adoption of AI-driven disease diagnosis methodologies for global health improvement.https://dergipark.org.tr/en/download/article-file/3649789artificial intelligencedeep learningexplainable aimedical imagingtuberculosis diagnosis |
| spellingShingle | Cem Özkurt Improving Tuberculosis Diagnosis using Explainable Artificial Intelligence in Medical Imaging Journal of Mathematical Sciences and Modelling artificial intelligence deep learning explainable ai medical imaging tuberculosis diagnosis |
| title | Improving Tuberculosis Diagnosis using Explainable Artificial Intelligence in Medical Imaging |
| title_full | Improving Tuberculosis Diagnosis using Explainable Artificial Intelligence in Medical Imaging |
| title_fullStr | Improving Tuberculosis Diagnosis using Explainable Artificial Intelligence in Medical Imaging |
| title_full_unstemmed | Improving Tuberculosis Diagnosis using Explainable Artificial Intelligence in Medical Imaging |
| title_short | Improving Tuberculosis Diagnosis using Explainable Artificial Intelligence in Medical Imaging |
| title_sort | improving tuberculosis diagnosis using explainable artificial intelligence in medical imaging |
| topic | artificial intelligence deep learning explainable ai medical imaging tuberculosis diagnosis |
| url | https://dergipark.org.tr/en/download/article-file/3649789 |
| work_keys_str_mv | AT cemozkurt improvingtuberculosisdiagnosisusingexplainableartificialintelligenceinmedicalimaging |