MediGenius AI:Fracture Recovery Revolutionized Using AI
| dc.contributor.advisor | DR. MOHAMMAD ASHRAFUZZAMAN KHAN | |
| dc.contributor.author | Md. Khurshid Jahan | |
| dc.contributor.author | Ashrin Mobashira Shifa | |
| dc.contributor.author | Rokaeya Sharmin | |
| dc.contributor.id | 1922079042 | |
| dc.contributor.id | 1922216042 | |
| dc.contributor.id | 1911993042 | |
| dc.coverage.department | Electrical and Computer Engineering | |
| dc.date.accessioned | 2026-04-22 | |
| dc.date.accessioned | 2026-04-22T06:34:40Z | |
| dc.date.available | 2026-04-22T06:34:40Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | This study introduces an innovative approach to fracture recovery utilizing artificial intelligence (AI) technology. A dataset comprising 1012 X-ray images, including 331 instances of fractures, sourced from St. Mariyaam Diagnostic Center, was meticulously annotated for nine distinct fracture types. Collaborative efforts with Dr. Rakibul Islam and Md. Asiful Rahman Maruf ensured accurate labeling and enriched the dataset with corresponding NLP descriptions and patient adviceAnticipating future enhancements, the integration of diffusion models is proposed, with the aim of synthesizing high-fidelity X-ray images. This development holds substantial promise in redefining fracture recovery procedures. | |
| dc.description.degree | Undergraduate | |
| dc.identifier.cd | 600000907 | |
| dc.identifier.print-thesis | To be assigned | |
| dc.identifier.uri | https://repository.northsouth.edu/handle/123456789/1648 | |
| dc.language.iso | en | |
| dc.publisher | North South University | |
| dc.rights | ©NSU Library | |
| dc.title | MediGenius AI:Fracture Recovery Revolutionized Using AI | |
| oaire.citation.endPage | 29 | |
| oaire.citation.startPage | 1 |
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