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--- |
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license: apache-2.0 |
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datasets: |
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- SilpaCS/Augmented_alzheimer |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-224 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- Alzheimer |
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- Stage-Classifier |
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- SigLIP2 |
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--- |
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# **Alzheimer-Stage-Classifier** |
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> **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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```py |
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Classification Report: |
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precision recall f1-score support |
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MildDemented 0.9634 0.9860 0.9746 8960 |
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ModerateDemented 1.0000 1.0000 1.0000 6464 |
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NonDemented 0.8920 0.8910 0.8915 9600 |
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VeryMildDemented 0.8904 0.8704 0.8803 8960 |
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accuracy 0.9314 33984 |
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macro avg 0.9364 0.9369 0.9366 33984 |
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weighted avg 0.9309 0.9314 0.9311 33984 |
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``` |
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--- |
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## **Label Classes** |
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The model classifies input images into the following stages of Alzheimer’s disease: |
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``` |
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0: MildDemented |
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1: ModerateDemented |
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2: NonDemented |
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3: VeryMildDemented |
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``` |
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--- |
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## **Installation** |
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```bash |
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pip install transformers torch pillow gradio |
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``` |
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--- |
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## **Example Inference Code** |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor, SiglipForImageClassification |
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from PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "prithivMLmods/Alzheimer-Stage-Classifier" |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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# ID to label mapping |
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id2label = { |
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"0": "MildDemented", |
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"1": "ModerateDemented", |
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"2": "NonDemented", |
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"3": "VeryMildDemented" |
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} |
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def classify_alzheimer_stage(image): |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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prediction = {id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
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return prediction |
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# Gradio Interface |
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iface = gr.Interface( |
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fn=classify_alzheimer_stage, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(num_top_classes=4, label="Alzheimer Stage"), |
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title="Alzheimer-Stage-Classifier", |
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description="Upload a brain scan image to classify the stage of Alzheimer's: NonDemented, VeryMildDemented, MildDemented, or ModerateDemented." |
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) |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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--- |
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## **Applications** |
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* **Early Alzheimer’s Screening** |
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* **Clinical Diagnosis Support** |
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* **Longitudinal Study & Disease Monitoring** |
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* **Research on Cognitive Decline** |
