Improving Medical Image Classification Using Vision transformers

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dc.contributor.authorYasser, Torki
dc.contributor.authorSupervisor: Nour elhouda, CHALABI
dc.date.accessioned2025-07-08T11:09:43Z
dc.date.available2025-07-08T11:09:43Z
dc.date.issued2025-06-15
dc.description.abstractThe integration of Vision Transformers (ViTs) into the field of medical imaging has opened new avenues for accurate, data-driven diagnostics by leveraging self-attention mechanisms to capture global contextual information. in this project we explored the application of ViTs to various medical imaging tasks, including classification, segmentation, and anomaly detection in X-raysour project utilizes the Vision Transformer (ViT) deep learning technique to accurately classify medical images from a chest X-ray dataset, demonstrating its effectiveness in various fields like computer vision and medical applications.
dc.identifier.urihttps://repository.univ-msila.dz/handle/123456789/46801
dc.language.isoen
dc.publisherMohamed Boudiaf University of M'sila
dc.subjectVision Transformer
dc.subjectdeep learning
dc.subjectConvolutional Neural Networks
dc.subjectmedical image classification
dc.subjectChest X-ray
dc.subjectComputer vision
dc.subjecthyperparameters
dc.subjectmodel efficiency
dc.subjecthealthcare improvement
dc.titleImproving Medical Image Classification Using Vision transformers
dc.typeThesis

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