Fine-Tuned Models and and Multiple Appendices and Raw Data for Brain Tumor Classification Using SVM+HOG, ResNet18, ViT-B/16, and SimCLR

1: paper title : Trade-off Analysis of Classical and Deep Learning Models for Robust Brain Tumor Detection. 2: Please see the GitHub repository for code and usage instructions: https://github.com/yangzi334/BrainTumorClassification 3: uploading contents include: base_Vit_B_16_new.zip best_vit_overall.pth — Final fine-tuned ViT-B/16 model weights with best overall validation performance. best_vit_run1.pth — Best checkpoint for ViT-B/16 from run 1 (different random seed or split). best_vit_run2.pth — Best checkpoint for ViT-B/16 from run 2. best_vit_run3.pth — Best checkpoint for ViT-B/16 from run 3. base_resnet18_new.zip best_resnet18_overall.pth — Final fine-tuned resnet18 model weights with best overall validation performance. best_resnet18_run1.pth — Best checkpoint for resnet18 from run 1 (different random seed or split). best_resnet18_run2.pth — Best checkpoint for resnet18 from run 2. best_resnet18_run3.pth — Best checkpoint for resnet18 from run 3. base_simclr_new.zip best_simclr_overall.pth — Final fine-tuned simclr model weights with best overall validation performance. best_simclr_run1.pth — Best checkpoint for simclr from run 1 (different random seed or split). best_simclr_run2.pth — Best checkpoint for simclr from run 2. best_simclr_run3.pth — Best checkpoint for simclr from run 3. best_simclr_encoder_only.pth — SimCLR encoder weights only (trained with contrastive learning, no classifier head). Can be used for downstream tasks base_SVN_HOG_new.zip best_svm_hog_model.pkl — Final support vector machine (SVM) model trained with HOG features. Saved using joblib for easy loading and reuse. base tumor data.zip Contains the full brain tumor image dataset split into training, validation, and test sets: Each folder (train/, valid/, test/) includes: A COCO-format annotation file named _annotations.coco.json In the train folder, it includes fixed_annotations.coco.json Corresponding brain MRI image files in .jpg format Multiple Appendix 1: left side: shows an original non-tumor image and its HOG visualization, its file name is ‘2256_jpg.rf.3afd7903eaf3f3c5aa8da4bbb928bc19.jpg’. right side: shows an original tumor image and its HOG visualization, its file name is ‘12_jpg.rf.21eba5a77b6113c9d7f9b182092a02d3.jpg’. Multiple Appendix 2: Figure 1 The final average of training accuracy and validation accuracy. Figure 2: Training loss and validation loss Figure 3: Total training time across all four models Figure 4: Validation data classification table and Confusion Matrix Figure 5: Classification results across all four models, with and without augmentation on unseen test data. Figure 6: Confusion Matrix results across all four models, with and without augmentation on unseen test data. Figure 7: Accuracy and loss over epochs on training and validation data during the training process.