Remote Assistance for Bone-Fractured Patients using Deep Learning Models
Remote diagnosis enables healthcare professionals to evaluate and diagnose patients from a distance using telecommunication technologies, enhancing healthcare delivery by improving accessibility, especially for those in remote or underserved areas. One of the significant sustainability challenges in...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2423326 |
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| _version_ | 1846119942878920704 |
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| author | Nallakaruppan Kailasanathan Sivaramakrishnan Somayaji Mohamed Baza Gautam Srivastava SenthilKumaran Ulaganathan Gokul Yenduri Vaishali Ravindranath Maazen Alsabaan |
| author_facet | Nallakaruppan Kailasanathan Sivaramakrishnan Somayaji Mohamed Baza Gautam Srivastava SenthilKumaran Ulaganathan Gokul Yenduri Vaishali Ravindranath Maazen Alsabaan |
| author_sort | Nallakaruppan Kailasanathan |
| collection | DOAJ |
| description | Remote diagnosis enables healthcare professionals to evaluate and diagnose patients from a distance using telecommunication technologies, enhancing healthcare delivery by improving accessibility, especially for those in remote or underserved areas. One of the significant sustainability challenges in remote medical diagnostics is offering timely assistance to vulnerable groups like the elderly, disabled, mentally impaired individuals, and wounded military personnel in combat zones. This becomes particularly difficult in emergencies when rapid analysis of medical records is needed, especially if the data is stored on secure blockchain networks. The proposed work addresses these challenges by deploying a comprehensive framework for large-scale analysis, utilizing both document and image classification for dual validation. It integrates advanced techniques such as Inception V3, VGG-16, VGG-19, RESNET-50, and Densenet-201 for bone fracture detection, with Inception V3 achieving the highest accuracy of 95.1%. In addition, a Document Classification Analysis (DCA) method is proposed, which automatically classifies the severity of fractures. Object detection techniques are also introduced for detecting minor fractures using region-based image segmentation, ensuring precise diagnosis even for subtle injuries. This pioneering integration of technologies provides a holistic solution for remote medical diagnostics. |
| format | Article |
| id | doaj-art-b201c461c9334efb8a94f11a5cbda689 |
| institution | Kabale University |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-b201c461c9334efb8a94f11a5cbda6892024-12-16T16:13:01ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2423326Remote Assistance for Bone-Fractured Patients using Deep Learning ModelsNallakaruppan Kailasanathan0Sivaramakrishnan Somayaji1Mohamed Baza2Gautam Srivastava3SenthilKumaran Ulaganathan4Gokul Yenduri5Vaishali Ravindranath6Maazen Alsabaan7Balaji Institute of Modern Management, Sir Balaji University, Pune, IndiaSchool of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, IndiaDepartment of Computer science, college of Charleston, Charleston, South Carolina, USADepartment of Mathematics and Computer Science, Brandon University, Brandon, CanadaSchool of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, IndiaDepartment of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, IndiaDepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaRemote diagnosis enables healthcare professionals to evaluate and diagnose patients from a distance using telecommunication technologies, enhancing healthcare delivery by improving accessibility, especially for those in remote or underserved areas. One of the significant sustainability challenges in remote medical diagnostics is offering timely assistance to vulnerable groups like the elderly, disabled, mentally impaired individuals, and wounded military personnel in combat zones. This becomes particularly difficult in emergencies when rapid analysis of medical records is needed, especially if the data is stored on secure blockchain networks. The proposed work addresses these challenges by deploying a comprehensive framework for large-scale analysis, utilizing both document and image classification for dual validation. It integrates advanced techniques such as Inception V3, VGG-16, VGG-19, RESNET-50, and Densenet-201 for bone fracture detection, with Inception V3 achieving the highest accuracy of 95.1%. In addition, a Document Classification Analysis (DCA) method is proposed, which automatically classifies the severity of fractures. Object detection techniques are also introduced for detecting minor fractures using region-based image segmentation, ensuring precise diagnosis even for subtle injuries. This pioneering integration of technologies provides a holistic solution for remote medical diagnostics.https://www.tandfonline.com/doi/10.1080/08839514.2024.2423326 |
| spellingShingle | Nallakaruppan Kailasanathan Sivaramakrishnan Somayaji Mohamed Baza Gautam Srivastava SenthilKumaran Ulaganathan Gokul Yenduri Vaishali Ravindranath Maazen Alsabaan Remote Assistance for Bone-Fractured Patients using Deep Learning Models Applied Artificial Intelligence |
| title | Remote Assistance for Bone-Fractured Patients using Deep Learning Models |
| title_full | Remote Assistance for Bone-Fractured Patients using Deep Learning Models |
| title_fullStr | Remote Assistance for Bone-Fractured Patients using Deep Learning Models |
| title_full_unstemmed | Remote Assistance for Bone-Fractured Patients using Deep Learning Models |
| title_short | Remote Assistance for Bone-Fractured Patients using Deep Learning Models |
| title_sort | remote assistance for bone fractured patients using deep learning models |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2423326 |
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