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Classification of chemically modified red blood cells in microflow using machine learning video analysis

R. K. Rajaram Baskaran, A. Link, B. Porr and T. Franke

DOI

Prerequisites

  • Python 3.10.6
  • Tensorflow 2.11.0
  • Keras
  • OpenCV
  • NumPy
  • Matplotlib
  • tqdm

Usage

  1. run main.py to train, create, validate and test the model.
  2. run plots.py to generate the plots as seen in the paper.

main.py <option_name>

Train, validate and test (native vs chem. mod.) RBCs.

Options:

  • FA: Classification of native vs formaldehyde
  • DA: Classification of native vs diamide
  • GA: Classification of native vs glutaraldehyde
  • MIX: Classification of native vs random mix of formaldehyde, diamide, glutaraldehyde

This generates all results in the directory results_<option>.

runall.sh

Runs all option: FA, DA, GA and MIX.

  • Foreground: Shows the accuracy and loss.
  • Background: nohup ./runall.sh &. You can log out and it will continue.

Modules

plots.py

Loads accuracy_and_loss_values.json and plots accuracy, loss and probability predictions.

video_processor.py

Labels the videos, subtracts the background, and returns them as NumPy arrays.

Tests

test_get_videos.py

Tests loading videos from the file directory.

test_bg_sub.py

Performs background subtraction, displays processed video.