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Update app.py
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app.py
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# Title and description
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title = "
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description = """
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### Description
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This app classifies skin cancer images into different categories using an AI model. 🖼️✨
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Upload your own image or use one of the examples to see the results.
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**DISCLAIMER⚠️**\n
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**This demo is for educational and informational purposes only**.It is not intended to provide a medical diagnosis, nor should it be considered a substitute for professional medical advice, diagnosis, or treatment. We are not liable for any misclassification of skin cancer images. If you have concerns about your health, please consult a healthcare professional.
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### Descrizione
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Questa app classifica le immagini di cancro della pelle in diverse categorie utilizzando un modello che utilizza intelligenza artificiale. 🖼️✨
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Carica la tua immagine o usa uno degli esempi elencati qui sotto per vedere i risultati.
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**AVVISO⚠️**\n
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Questa demo è solo a scopo educativo e informativo. Non è intesa a fornire una diagnosi medica, né deve essere considerata un sostituto di un consulto medico professionale, una diagnosi o un trattamento. Non siamo responsabili per eventuali errori nella classificazione delle immagini di cancro della pelle. Se hai preoccupazioni sulla tua salute, consulta un professionista sanitario.
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### About Us
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We are researchers in the [AImageLab](https://aimagelab.ing.unimore.it/imagelab/) 🔬 of the University of Modena and Reggio Emilia.
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Some of us are working on **Artificial Intelligence for Medical Imaging** 🧠🧑⚕️👩⚕️🥼
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\n
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Siamo dei ricercatori del laboratorio [AImageLab](https://aimagelab.ing.unimore.it/imagelab/) 🔬 dell' Università di Modena e Reggio Emilia.
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Alcuni di noi lavorano sul **Medical Imaging con uso di Intelligenza Artificiale** 🧠🧑⚕️👩⚕️🥼
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### Technical Details 🤓
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The architecture used is a pre- trained Vision Transformer (ViT) on the ImageNet21k, with a fine-tuning on the [HAM10k dataset](https://huggingface.co/datasets/marmal88/skin_cancer) and a modified head to accommodate for the classes: Benign keratosis-like lesions, Basal cell carcinoma, Actinic keratoses, Vascular lesions, Melanocytic nevi, Melanoma, Dermatofibroma.
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The best validation accuracy obtained was 0.9695. However this score is not a good indicator of performance given the class imbalances present in the dataset.
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### Credits
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Original model trained and uploaded on Hugging Face by user [Anwarkh1](https://huggingface.co/Anwarkh1).
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HF Space dapted and updated by [Ettore Candeloro](https://ettorecandeloro.me/)
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"""
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# Load the model and launch the app with title, description, examples,
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demo = gr.load("models/Anwarkh1/Skin_Cancer-Image_Classification", examples=examples, title=title, description=description).launch()
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# Title and description
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title = "Melanoma skin Cancer detection "
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# Load the model and launch the app with title, description, examples,
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demo = gr.load("models/Anwarkh1/Skin_Cancer-Image_Classification", examples=examples, title=title, description=description).launch()
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