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--- |
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license: mit |
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pipeline_tag: image-classification |
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tags: |
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- medical |
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--- |
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## Model Details |
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This model is trained on 224X224 Grayscale images which are CT-scans |
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that are transformed into JPEG. The model is a finetuned version of |
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[Swin Transformer (tiny-sized model)](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224). |
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I also used this tutorial.[Swin Transformer (tiny-sized model)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb#scrollTo=UX6dwmT7GP91). |
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## Uses |
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The model can be used to classify JPEG images of CT scans into either cancer positive or |
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Cancer negative groups. |
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I think it would work okay for any image classification task. |
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## Training Data |
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The model was trained on data originally obtained from the National Cancer Institute |
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Imaging Data Commons. https://portal.imaging.datacommons.cancer.gov/explore/ |
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The data set used consisted of about 11,000 images which were transformed CT scans |
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some of which contained Cancerous Nodules and some that did not. |
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## How to Use |
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```python |
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from huggingface_hub import hf_hub_download |
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from PIL import Image |
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abc= hf_hub_download(repo_id="oohtmeel/swin-tiny-patch4-finetuned-lung-cancer-ct-scans", |
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filename="_X000a109d-56da-4c3f-8680-55afa04d6ae0.dcm.jpg.jpg") |
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image = Image.open(abc) |
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processor = AutoImageProcessor.from_pretrained("oohtmeel/swin-tiny-patch4-finetuned-lung-cancer-ct-scans") |
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model = AutoModelForImageClassification.from_pretrained("oohtmeel/swin-tiny-patch4-finetuned-lung-cancer-ct-scans") |
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inputs = processor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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logits = outputs.logits |
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predicted_class_idx = logits.argmax(-1).item() |
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print("Predicted class:", model.config.id2label[predicted_class_idx]) |
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``` |
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