SKIN-CANCER-DETECTOR

SKIN CANCER 🔬🧪DETECTION SYSTEM

SKIN-CANCER-DETECTOR is a web application that detects malignant and benign skin cancer. It leverages computer vision and image recognition techniques to determine if a patient has skin cancer or not. The application offers a friendly and intuitive web interface for users who are non technical to easily use the AI.

Folder Structure

📂 SKIN-CANCER-DETECTOR

|

├── 📄 README.md

├── 📂 datasets

│ ├── 📂 test

│ │ ├── 📂 benign

│ │ ├── 📂 malignant

│ ├── 📂 train

│ │ ├── 📂 benign

│ │ ├── 📂 malignant

├── 📂 model

│ ├── 📄 saved_model.h5

├── 📂 uploads

├── 📂 static

│ ├── 📂 css

│ │ └── 📄 styles.css

│ ├── 📂 images

│ │ └── 📄 header-image.jpg

│ ├── 📂 js

│ │ └── 📄 script.js

│ └── 📄 favicon.ico

├── 📂 templates

│ ├── 📄 index.html

│ └── 📄 result.html

├── 📄 CancerDetector.ipynb

├── 📄 app.py

└── 📄 requirements.txt

The project folder structure consists of the following files and folders:

• 📄 README.md: This file contains the documentation and information about the FOOT-FORECASTER web application, including how to use it and any additional details.

• 📂 data: This folder contains the data files used for preprocessing and training the machine learning models.

• 📂 models: This folder contains the trained machine learning model in the form of an h5 file named saved_model.h5.

• 📂 static: This folder contains the static assets used in the web application, including CSS files, JavaScript files, and a favicon.ico file.

  • 📂 css: This subfolder contains the CSS stylesheets used for styling the web application, specifically the styles.css file.

  • 📂 js: This subfolder contains the JavaScript files used for the web application's functionality, including the script.js files.

  • 📂 images: This subfolder contains the image files used for the web application's frontend, including the header-image.jpg file.

• 📂 templates: This folder contains the HTML templates used for rendering the web pages, specifically the index.html and the result.html file.

• 📄 CancerDetector.ipynb: This Jupyter Notebook file is used for training machine learning models for the image detection.

• 📄 app.py: This file contains the Flask microservice backend for the FOOT-FORECASTER web application. It includes the machine learning model and handles the data requests and responses.

• 📄 requirements.txt: This file lists the project's dependencies and their versions for easy installation.

Usage

To use the SKIN-CANCER-DETECTOR web application, follow these steps:

1. Clone or download this project repository.

2. Install the required dependencies by running the following command:

   pip install -r requirements.txt
   

3. Open the CancerDetector.ipynb notebook and execute it to train the machine learning models.

4. Run the Flask microservice backend by executing the following command:

   python app.py
   

5. Open a web browser and access the SKIN-CANCER-DETECTOR web application by navigating to http://localhost:5000.

6. Using the upload button on the web user interface, upload an image you wish to test for cancer.

7. View the test results on the results page.

Code Explanation

The SKIN-CANCER-DETECTOR web application is implemented using HTML, CSS, JavaScript and Flask. Here's a breakdown of the different components:

• JavaScript: This file handles the interactivity of the web application, including attaching event listeners, updating the user interface on user input.

• Flask: The Flask microservice backend is implemented in the app.py file. It handles the routing and data requests for the web application.

Troubleshooting

If you encounter any issues or errors while using the SKIN-CANCER-DETECTOR web application, consider the following:

• Double-check that all the necessary files and folders are present in the correct locations, as described in the folder structure section.

• Ensure that you have Python installed on your system, and the required dependencies are installed by running pip install -r requirements.txt.

• Verify that the data files are located in the datasets folder.

• If you encounter any issues with the machine learning models, make sure you have successfully preprocessed the data and trained the models using the provided Jupyter Notebook files (CancerDetector.ipynb).

• If the Flask microservice backend fails to run, check that there are no errors in the app.py file and that the required dependencies are installed.

If the problem persists, feel free to open an issue in the GitHub repository for further assistance.

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This README file provides an overview of the FOOT-FORECASTER web application, its folder structure, usage instructions, code explanation, and troubleshooting tips. Use it as a guide to understand and utilize the SKIN-CANCER-DETECTOR app.