itistamtran/vit_brain_tumor_multiclass_v2

Vision Transformer for Brain Tumor Multiclass Classification (v2)

This model is a fine-tuned Vision Transformer (ViT) for multiclass brain tumor MRI classification. It predicts one of the following five classes:

• Glioma
• Meningioma
• Pituitary Tumor
• No Tumor
• Unknown

The model is integrated into an online diagnostic support platform at https://www.medscanai.net/.

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Model Details

• Base Model: google/vit-base-patch16-224-in21k
• Architecture: Vision Transformer
• Image Size: 224 × 224
• Number of Classes: 5
• License: CC-BY-NC 4.0
• Intended Use: Research and educational purposes only
• Not for clinical diagnosis

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Dataset

The primary dataset is sourced from the Kaggle "Brain Tumor MRI Dataset" by Masoud Nickparvar.
Additional "unknown" category samples were collected from publicly available online sources to evaluate robustness.

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Training

• 20 epochs using Hugging Face Trainer
• Learning rate: 3e-5
• Augmentation: flips, rotation, brightness/contrast jittering
• Optimizer: AdamW
• Evaluation metric: Accuracy

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Performance

Class	Precision	Recall	F1-score
Glioma	0.97	0.98	0.98
Meningioma	0.97	0.96	0.97
No Tumor	0.99	1.00	0.99
Pituitary	0.99	0.99	0.99
Unknown	1.00	0.99	1.00

Overall accuracy: 98 percent

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How to Use

from transformers import AutoModelForImageClassification, AutoImageProcessor
from PIL import Image
import torch

model = AutoModelForImageClassification.from_pretrained("itistamtran/vit_brain_tumor_multiclass_v2")
processor = AutoImageProcessor.from_pretrained("itistamtran/vit_brain_tumor_multiclass_v2")

image = Image.open("mri_image.png")
inputs = processor(images=image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    preds = outputs.logits.argmax(dim=1)
print(preds)