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Skin Cancer Detection & Management system using Flask, Python and MySQL


The Skin Cancer Detection and Management System utilizes Flask, Python, and a MySQL database, incorporating MobileNet V2 and Grad-CAM for real-time, insightful skin cancer diagnosis from lesion images. This innovative tool streamlines the early detection and monitoring process, offering clinicians a powerful aid in patient care.


The Skin Cancer Detection and Management System is a cutting-edge solution designed for real-time identification and monitoring of skin cancer. Developed using Flask and Python, with a MySQL database for robust data management, this system leverages the power of MobileNet V2, a highly efficient convolutional neural network, for the accurate prediction of skin cancer from lesion images. It not only offers real-time analysis but also enhances understanding through model interpretation with Gradient-weighted Class Activation Mapping (Grad-CAM). This visualization technique highlights the areas of the image most influential in the model’s prediction, providing clinicians with valuable insights into the diagnostic process. The system’s intuitive interface and backend support ensure a seamless experience for users, making it an invaluable tool in the early detection and management of skin cancer.

Once purchase the project Download Link will be available instantly.

Project Includes:

  1. Demo  Video Url
  2. Flask App source code
  3. Model building code Repo
  4. MySQL Schema file
  5. Full Project Presentation
  6. Model Link to download
  7. Online support

Features Of This Project

Admin :

  1. User Management –  Manage Admin User/Doctors/Nurses.
  2. Dashboard – Showing complete view of the number of doctors/nurses and pending cases.
  3. Report –  Can download the report based on the date filter and export it to Excel.


  1. Update profile –  Nurse can edit/update her profile.
  2. View Report – Nurse can view historical report previously handled by her with associated dermatologist involved.
  3. Model Prediction –  Nurse can upload the skin lesion image of patient to get the real-time prediction of computer vision model along with model interpretation with highlighted region for showing where the model is seeing. Nurse can also add her feedback comment and further allocate the case to the available dermatologist for further review.


  1. Update profile – Dermatologists can edit/update his/her profile.
  2. View Report – Doctor can view historical report resolved and pending with him for review.
  3. Alloted Case –  Doctor can view all the alloted cases to him  where by referring the model prediction result along with model interpretation and nurse feedback he can add his own feedback with final diagonisis.

Technology Used 

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. MySQL: MySQL  database has been used as database for the project
  7. Flask: Project has been developed using the Flask Framework

Supported Operating System

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.7, PIP, Flask.
  4. Linux : We can run this project also on all versions of Linux operating system Mac : We can also easily configured this project on Mac operating system.

Installation Steps :-

  1. Install Python >=3.7
  2. Download and Install MySQL Workbench
  3. Run SQL_SCHEMA_MYSQL.sql file on MySQL Workbench
  4. Install all dependencies cmd –python -m pip install –-user -r requirements.txt
  5. Finally, run cmd – python
  6. Admin User Id-
  7. Admin Password – admin

Important Note:

Please be advised that the Skin Cancer Detection and Management Web App described herein is developed as an academic project and is intended for demonstration and educational purposes only. It should not be used for clinical diagnosis or treatment without undergoing thorough testing and validation in accordance with established medical standards and guidelines. This application is a representation of potential technological advancements in healthcare but has not been certified by medical associations for clinical use. Users are strongly discouraged from relying on this application for clinical purposes until it has been rigorously tested and approved by relevant regulatory bodies and medical professionals. We urge all users to exercise caution and consult with qualified healthcare providers for any medical diagnosis or treatment options.


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