Artificial intelligence techniques for medical decision support : application to cardiovascular diseases
| dc.contributor.author | BOUKHAMLA, Assia | |
| dc.date.accessioned | 2025-07-22T10:22:52Z | |
| dc.date.available | 2025-07-22T10:22:52Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Cardiovascular disease (CVD) remains a leading cause of death worldwide, and early diagnosis is critical to improving patient outcomes. Despite advances in diagnostic imaging, such as MRI and CT, challenges still need to be solved in terms of cost, long processing times, and the need for specialized expertise. Deep learning (DL) techniques, particularly convolutional neural networks (CNNs) and vision transformers (ViTs) have shown great promise in medical image analysis, particularly for segmentation and clas- sification tasks. However, limitations still need to beimproved across divers eclinical datasets, as well as the need for large annotated datasets to train models effectively. | |
| dc.format | ||
| dc.identifier.uri | https://dspace.univ-annaba.dz//handle/123456789/4069 | |
| dc.language.iso | en | |
| dc.publisher | Université Badji Mokhtar Annaba | |
| dc.subject | cardiovascular diseases; deep Learning; vision transformers; cardiovas- cular Image segmentation; ensemble Learning; transfer Learning | |
| dc.title | Artificial intelligence techniques for medical decision support : application to cardiovascular diseases | |
| dc.title.alternative | Techniques de l'intelligence artificielle pour l'aide à la décision médicale : application aux maladies cardiaques | |
| dc.type | Thesis |