READ ME File for 'Trained lightweight convolutional neural network (modified R-MOD) for detecting phytoplankton in imaging flow cytometry images'

Dataset DOI:10.5258/SOTON/XXXXXX

ReadMe Author: Anthony Lindley, orcid.org/0009-0005-8447-969X	

This dataset supports the thesis entitled Design, Fabrication and Characterisation of a Low-Cost, Acoustically Focussed Imaging Flow Cytometer for Automated Analysis of Phytoplankton
AWARDED BY: University of Southampton
DATE OF AWARD: 2023

DESCRIPTION OF THE DATA

This dataset contains:

Convolutional Neural Network model (in pyTorch state dictionary format) from the thesis Design, Fabrication and Characterisation of a Low-Cost, Acoustically Focussed Imaging Flow Cytometer for Automated Analysis of Phytoplankton. The model is trained on 150 imaging flow cytometry images containing cells of Rhodomonas salina, and outputs a 2D probability density map representing its confidence in the presence of a phytoplankton cell in each pixel. 


DESCRIPTION OF FILES:

Trained rmod network - pytorch state dictionary file containing the trained CNN object detection model