Random Forest (RF) is an ensemble approach that uses multiple decision trees to make predictions from data. The decision trees are trained using a process called bootstrapping, which involves sampling the data with replacement. Each decision tree in the RF makes predictions based on the features in the data, and the majority voting calculates the final prediction. We designed our algorithm as a CNN-RF algorithm to optimally segment spores and classify layers. The CNN is first trained on image data to convert high-dimensional 2D TEM images into vectors of real values, which are then used as input features for decision trees in the RF algorithm. Bacillus thuringiensis ATCC 35646 cells were grown on BBLK agar plates and set to incubate at 30C overnight, and then stored at 4C overnight to allow sporulation.
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