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https://github.com/muhammadunaisak/dermato
Skin cancer detection app full source code updated soon
https://github.com/muhammadunaisak/dermato
Last synced: 3 days ago
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Skin cancer detection app full source code updated soon
- Host: GitHub
- URL: https://github.com/muhammadunaisak/dermato
- Owner: muhammadunaisak
- Created: 2023-11-09T14:28:43.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-30T02:42:07.000Z (10 months ago)
- Last Synced: 2024-01-30T03:33:35.743Z (10 months ago)
- Language: Java
- Homepage:
- Size: 16.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Dermato
Diagnose skin image classification and make appointment for Dermatologists.
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## Table Of Contents
* [About the Project](#about-the-project)
* [Getting Started](#getting-started)
* [Contributing](#contributing)
* [Authors](#authors)
* [Acknowledgements](#acknowledgements)
## About The Project
#Screen Shot Home page Web.
#Screen Shot Home page App.
Skin cancer is the most common form of cancer worldwide, with over 3 million cases diagnosed annually in the United States alone. It occurs when the cells in the skin start to grow abnormally and out of control, leading to the development of tumors or lesions. The main cause of skin cancer is exposure to ultraviolet (UV) radiation from the sun or artificial sources such as tanning beds.There are several types of skin cancer, including basal cell carcinoma, squamous cell carcinoma, and melanoma. Basal cell carcinoma and squamous cell carcinoma are the most common types of skin cancer and are often treatable with early detection and intervention. However, melanoma is the most deadly form of skin cancer and can spread quickly to other parts of the body if not caught early.
##This is an Android Application in which user can diagnose their skin and make appointment for Dermatologists
skin cancer detection system using Python and appointment system to get skin specialists. The system will be designed to analyze images of skin lesions uploaded by users and provide a risk assessment of the likelihood that the lesion is benign or malignant.The application here uses machine learning algorithms to analyze the images and classify them based on features such as color, shape, and texture. The system will be trained on a large dataset of images of skin lesions to improve the accuracy of the classification.
Users will be able to upload images of their skin lesions through android application. The system will provide a risk assessment based on the analysis of the uploaded image and recommend follow-up care, such as scheduling an appointment with a dermatologist and able to chat with doctor.The application will also include a database to store information about the disease.
Overall, the proposed skin cancer detection system using Python has the potential to greatly improve the accuracy and efficiency of skin cancer diagnosis, allowing individuals to seek medical attention in a timely manner and potentially saving lives. This project comes under the category of website and Android Application.
## Getting Started
## Contributing
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are **greatly appreciated**.
* If you have suggestions for adding or removing projects, feel free to [open an issue](https://github.com/muhammadunaisak/Dermato/issues/new) to discuss it, or directly create a pull request after you edit the *README.md* file with necessary changes.
* Please make sure you check your spelling and grammar.
* Create individual PR for each suggestion.
* Please also read through the [Code Of Conduct](https://github.com/muhammadunaisak/Dermato/blob/main/CODE_OF_CONDUCT.md) before posting your first idea as well.
### Creating A Pull Request
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## Author
* **unais** - *Comp Sci Student* - [unais](https://github.com/muhammadunaisak) - **
## Techs Used Here
[](https://skillicons.dev)