app.py

import gradio as gr  

title="Thyroid Tumor Classification On Ultrasound Images"
article = "This study was made by S.Serdar Helli using HF Transformers ConvNext"

description=f'''
Thyroid nodule is one of the most common endocrine carcinomas. Due to its higher reveal ability and ability to distinguish between benign and malignant nodules in pathological features, ultrasonography has become the most widely used modality for finding and diagnosing thyroid cancer when compared to CT and MRI.
In this study, the purpose is the classification of thyroid tumors on ultrasound images with 2 different categories:
- Malign(1)
- Benign(0)
This study was made using HF Transformers :
- [ On Google Colab](https://colab.research.google.com/drive/1ueSq8Y_NmFr7NGdtS8FStI3d2HR-43LD?usp=sharing)
- [On Github](https://github.com/SerdarHelli/The-Classification-of-Thyroid-Tumors-on-UltraSound-Images-using-Deep-Learning-Methods)
- [ Using Keras and GradCam With MultiClasses  Medium Article](https://serdarhelli.medium.com/the-basic-classification-of-thyroid-tumors-on-ultrasound-images-using-deep-learning-methods-46f812d859ea)
The Dataset:
[Colombia National University presented an open access database of thyroid ultrasound images.](http://cimalab.unal.edu.co/?lang=es&mod=program&id=5)
Ref : Pedraza, Lina & Vargas, Carlos & Narváez, Fabián & Durán, Oscar & Muñoz, Emma & Romero, Eduardo. (2015). An open access thyroid ultrasound-image Database. Progress in Biomedical Optics and Imaging — Proceedings of SPIE. 9287. 10.1117/12.2073532. '''



examples=[["https://huggingface.co/spaces/SerdarHelli/ThyroidTumorClassification/resolve/main/example_Benign1.png"]]
gr.Interface.load("huggingface/SerdarHelli/ThyroidTumorClassificationModel",title=title,description=description,article=article,examples=examples).launch()