Pytorch Malaria classifier #43
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Updated environment file, added training, data prep, test prep and pytorch model
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Updated environment file, added training, data prep, test prep and pytorch model.
This binary classifier can be used to analyze benign and malignant microscopy images (binary so Infected vs Not Infected of Thin or Thick smears) from the National Library of Medicine dataset at https://lhncbc.nlm.nih.gov/LHC-research/LHC-projects/image-processing/malaria-datasheet.html. This model achieved a validation accuracy in the region of 90% on the augmented dataset (mainly geometric transformations such as rotations/horizontal and vertical flips of the original images) that was used to train it.
The RGB image needs to be reduced to size 50x50x3 before being analyzed by the model.
It is basically the same neural network architecture as proposed by https://www.kaggle.com/code/kushal1996/detecting-malaria-cnn/notebook adapted to Pytorch with some finetuning.