https://github.com/omar-karimov/arztai

Innovative digital health platform
https://github.com/omar-karimov/arztai

app cnn-classification deep-learning deployment health keras machine-learning medical-image-processing mri-images random-forest streamlit streamlit-dashboard tensorflow transfer-learning

Last synced: 8 days ago
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Innovative digital health platform

• Host: GitHub
• URL: https://github.com/omar-karimov/arztai
• Owner: Omar-Karimov
• Created: 2021-05-21T13:10:09.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-06-14T10:26:12.000Z (over 3 years ago)
• Last Synced: 2024-11-21T20:21:53.312Z (2 months ago)
• Topics: app, cnn-classification, deep-learning, deployment, health, keras, machine-learning, medical-image-processing, mri-images, random-forest, streamlit, streamlit-dashboard, tensorflow, transfer-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 24.9 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# ArztAI

### Innovative digital health platform

* Username: Admin

* Password: 537382

Deployed at  https://share.streamlit.io/omar-karimov/arztai/main/App.py

Artificial intelligence and machine learning are successfully applied in medicine and solve a wide range of problems, gradually transforming from an auxiliary tool into good assistants for medical personnel. The use of AI increases the efficiency of the doctor, saving him from performing a number of routine and complex tasks. Using Deep Learning to train an image recognition algorithm combined with human experience will provide more accurate diagnostics. Analyzing digital images at the pixel level can help detect pathological changes that the human eye can easily miss.

### About Project

In this project I have created app which can identify different types of diseases, such as brain tumor, COVID, pneumonia, and heart failure. In the implementation of this project used different methods of deep learning and machine learning. ArztAI deployed and hosted in Streamlit Sharing.

![image1](https://user-images.githubusercontent.com/68358028/119236773-1ad3c100-baee-11eb-997b-16edf4702fc6.png)

![image2](https://user-images.githubusercontent.com/68358028/119236842-68e8c480-baee-11eb-9381-d47e46b742a0.png)

![image3](https://user-images.githubusercontent.com/68358028/119236880-a64d5200-baee-11eb-9274-ed195f181b9a.png)

![image4](https://user-images.githubusercontent.com/68358028/119236926-f3c9bf00-baee-11eb-920c-59e241ebece0.png)

![image5](https://user-images.githubusercontent.com/68358028/119236961-2c699880-baef-11eb-93d0-799568ef1c2e.png)

![image6](https://user-images.githubusercontent.com/68358028/119236995-58851980-baef-11eb-83d6-8dcdee3b35c8.png)

![image7](https://user-images.githubusercontent.com/68358028/119237020-823e4080-baef-11eb-9735-ab9cfa778b64.png)

* Data Sets

* source: https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection

* source: https://www.kaggle.com/andrewmvd/heart-failure-clinical-data

* source: https://www.kaggle.com/gibi13/pneumonia-covid19-image-dataset?select=normal