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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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+
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+ # **Alzheimer-Stage-Classifier**
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+
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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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+
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+ ---
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+
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+ ## **Label Classes**
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+
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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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+ ```
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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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+ ---
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+
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+ ## **Installation**
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+
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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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+ ---
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+
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+ ## **Example Inference Code**
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+ ---
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+
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+ ## **Applications**
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+
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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**