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Swin-Tiny fine-tuned to classify cardiac MRI images as `cad` (coronary artery disease) or `healthy`. This checkpoint comes from the co-authored M.Sc. thesis *Novel Deep Learning Architecture for Predicting Heart Diseases based on Transformers and Attention Mechanism with Explainability Model* by Raedin Khaled Sakhri and Loutfi Boufeligha, supervised by Prof. Abdelouaheb Moussaoui (Ferhat Abbas University, Algeria, July 2022). The thesis compared eight models on the same task. This one is the fine-tuned Swin-Tiny, pretrained on ImageNet-1k. The Trainer logged about 6.1 hours of training for the 3 epochs; the thesis rounds this to about 7 hours. For comparison, in the thesis: ViT-Base 98.27%, DeiT 99.01% (overfitting noted), a Vision Transformer trained from scratch 75.73%, and a CNN 88%.

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