A Transformer-based ensemble frame work for Early diagnosis of genetic syndromes using facial feature analysis
| dc.contributor.author | BELHACINI , Amila | |
| dc.contributor.author | LEMOUCHI , Ala Rahma | |
| dc.contributor.author | HAZMOUNE , Samira | |
| dc.date.accessioned | 2026-02-18T09:16:59Z | |
| dc.date.available | 2026-02-18T09:16:59Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | This thesis ,titled "A Transformer-Based Ensemble Frame work for Early Diagnosis of Genetic syndromes Using Facial Feature Analysis", presents an advanced deep learning approach for early detection of pediatric genetic syndromes using facial images. The motivation stems from the need to assist clinicians with faster and more accurate diagnosis , as facial traits often contain key indicators of genetic abnormalities .To address the limitations of traditional convolutional models, this work integrates Transformer architectures capable of capturing global dependencies and fine-grained facial patterns that are essential for distinguishing between syndromic and non-syndromic faces. The proposed system employs three state-of-the-artTransformer-models- based-Vi-sion Transformer (ViT),Data-efficient Image Transformer(DeiT),and Swin Transformer (Swin-T) —which are combined through ensemble learning using Random Forest,XG-Boost , and Logistic Regression meta-classifiers. This hybrid strategy enhances generalization and robustness across diverse facial representations. Experimental results on a custom-built dataset demonstrate a significant improvement in accuracy, achieving 86%, validating the effectiveness of the ensemble Transformer framework for automated pediatric syndrome diagnosis. | |
| dc.identifier.uri | http://dspace.univ-skikda.dz:4000/handle/123456789/5863 | |
| dc.language.iso | en | |
| dc.publisher | Faculty of Sciences | |
| dc.title | A Transformer-based ensemble frame work for Early diagnosis of genetic syndromes using facial feature analysis | |
| dc.title.alternative | Artificial Intelligence | |
| dc.type | Masters degree thesis |