Fig. 5

Workflow utilizing supervised machine learning for classification and prediction. a A supervised machine learning approach first requires the algorithm to learn the task of classification/prediction, based on the training data. Conventional machine learning approaches require another set of algorithms for identifying, selecting and extracting the features from the images. The extracted features are then used for projecting the image into a high-dimensional feature space. The task of classification/prediction is then done over this feature space. b In contrast, deep learning identifies these features through its complex neural architecture, trying to mimic the human brain, without requiring additional steps for it. Once trained, these models tend to perform much faster and are suitable for real-time quantification