README.md

metadata

license: apache-2.0
datasets:
  - SilpaCS/Augmented_alzheimer
language:
  - en
base_model:
  - google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
  - Alzheimer
  - Stage-Classifier
  - SigLIP2

Alzheimer-Stage-Classifier

Alzheimer-Stage-Classifier is a multi-class image classification model based on google/siglip2-base-patch16-224, designed to identify stages of Alzheimer’s disease from medical imaging data. This tool can assist in clinical decision support, early diagnosis, and disease progression tracking.

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Label Classes

The model classifies input images into the following stages of Alzheimer’s disease:

0: MildDemented  
1: ModerateDemented  
2: NonDemented  
3: VeryMildDemented

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Installation

pip install transformers torch pillow gradio

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Example Inference Code

import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Alzheimer-Stage-Classifier"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# ID to label mapping
id2label = {
    "0": "MildDemented",
    "1": "ModerateDemented",
    "2": "NonDemented",
    "3": "VeryMildDemented"
}

def classify_alzheimer_stage(image):
    image = Image.fromarray(image).convert("RGB")
    inputs = processor(images=image, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()

    prediction = {id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))}
    return prediction

# Gradio Interface
iface = gr.Interface(
    fn=classify_alzheimer_stage,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(num_top_classes=4, label="Alzheimer Stage"),
    title="Alzheimer-Stage-Classifier",
    description="Upload a brain scan image to classify the stage of Alzheimer's: NonDemented, VeryMildDemented, MildDemented, or ModerateDemented."
)

if __name__ == "__main__":
    iface.launch()

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Applications

• Early Alzheimer’s Screening
• Clinical Diagnosis Support
• Longitudinal Study & Disease Monitoring
• Research on Cognitive Decline