metadata

license: apache-2.0
tags:
  - vision
  - image-classification
datasets:
  - sartajbhuvaji/Brain-Tumor-Classification
language:
  - en
pipeline_tag: image-classification

MRI-Reader(small-sized model)

MRI-Reader is a fine-tuned version of swin-base. It was introduced in this paper by Liu et al. and first released in this repository.

Model description

The Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose backbone for both image classification and dense recognition tasks. In contrast, previous vision Transformers produce feature maps of a single low resolution and have quadratic computation complexity to input image size due to computation of self-attention globally.

Source

How to use

Here is how to use this model to identify meningioma tumor from a MRI scan:

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

processor = AutoImageProcessor.from_pretrained("NeuronZero/MRI-Reader")
model = AutoModelForImageClassification.from_pretrained("NeuronZero/MRI-Reader")

# Dataset url: https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri
 
image_url = "https://storage.googleapis.com/kagglesdsdata/datasets/672377/1183165/Testing/meningioma_tumor/image%28112%29.jpg?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=databundle-worker-v2%40kaggle-161607.iam.gserviceaccount.com%2F20240326%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240326T125018Z&X-Goog-Expires=345600&X-Goog-SignedHeaders=host&X-Goog-Signature=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"
image = Image.open(requests.get(image_url, stream=True).raw)

inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])