Dynamic Viability Analysis of Drug-Treated Cancer Cells with Incremental Learning Algorithms (DVA)

Introduction

This project, DVA, develops a desktop application to classify drug-treated cancer cells using machine learning. It aims to automate the analysis of cell viability and death levels, enhancing the accuracy and speed of research in cancer treatments.

Features

• Automated Analysis: Automatically determine the viability of cancer cells from images.
• Advanced Image Processing: Includes Threshold algorithms, Contour Detection etc. to prepare images for machine learning analysis.
• Incremental Machine Learning Model: Uses an Incremental Machine learning model that adapts and improves over time.
• User-Friendly Interface: Designed to be accessible for both technical and non-technical users.

Installation

Clone the repository:

git clone https://github.com/emreozogul/DVA.git
cd DVA
pip install -r requirements.txt

Install the required packages:

pip install -r requirements.txt

or :

pip install eel opencv-python scikit-learn wxPython pandas numpy 

Run the application with:

python app.py

Libraries

Python

Eel - https://github.com/python-eel/Eel

OpenCV, Sklearn, Wx, Pandas, NumPy

JS Scripts

Tiff.js - https://github.com/seikichi/tiff.js

Canvastotiff.js - https://github.com/motiz88/canvas-to-tiff

Cropper.js - https://github.com/fengyuanchen/cropperjs

JQuery

Authors and Acknowledgement

• Emre Evcin
• Emre Özoğul
• Mehmet Eren Sönmez

Special thanks to our supervisors, Assoc. Prof. Dr. Kaya Oğuz and Prof. Dr. Zeynep Fırtına Karagonlar.

The project has earned the right to receive support from The Scientific and Technological Research Council of Turkey (TÜBİTAK).

Project Link: https://github.com/emreozogul/DVA