Skin Lesion Classification App is a Flask web application that allows users to select different models trained using TensorFlow to classify images of skin lesions. Users can upload their own images and choose a model to generate a report classifying the image. The app also includes tabs for Exploratory Data Analysis (EDA) and model comparison to provide users with more information about the available ML models.

• Introduction
• Features
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Project Structure

The Skin Lesion Classification App is designed to assist users in classifying skin lesions through machine learning models. It provides an interactive platform to upload images, select a model, and generate classification reports.

• Upload images for classification
• Select from various TensorFlow models
• Generate classification reports
• Exploratory Data Analysis (EDA) tab
• Model comparison tab

• Python 3.11
• Flask
• TensorFlow
• Other dependencies (specified in requirements.txt)

1. Clone the repository:

   git clone https://github.com/maxcollins457/SkinCancerApp.git
   cd SkinCancerApp

2. Install dependencies

   pip install -r requirements.txt

1. Run the flask app using

   python main.py

2. Open your browser and navigate to http://localhost:5000.

3. Explore the different tabs for image classification, EDA, and model comparison.

This is a brief overview of the project structure.

SkinCancerApp/

│

├── app/

│ ├── data/

│ │ └── CancerData.csv

│ ├── networks/

│ │ └── h5 keras models

│ ├── static/

│ │ ├── css/

│ │ ├── img/

│ │ └── js/

│ ├── templates/

│ │ └── html templates

│ └── views/

│ └── flask views

│ ├── __init__.py

│ ├── models.py

│

├── COLAB NOTEBOOKS/

│ └── ipynb notebooks

│

├── requirements.txt

├── README.md

└── main.py

• data: Processed metadata from the dataset.
• networks: Stores trained machine learning models and related files.
• static: Images, CSS and JS for aesthetic of the flask app.
• templates: HTML templates for the flask app.
• COLAB NOTEBOOKS: Jupyter notebooks for exploratory data analysis and model training.
• requirements.txt: Lists project dependencies.
• README.md: Project documentation.