Figure 8: Learning curves and performance of various classification algorithms.

From: Deep Learning in Label-free Cell Classification

Figure 8: Learning curves and performance of various classification algorithms.
Figure 8

(a) The learning curves of the training and test datasets in the tumor cell detection. Larger number of training data points decreases the cross entropy of the test dataset, which means the classifier is performing more accurately. However, the trend is opposite for the training dataset because the fitting error accumulates with a larger number of training data points. The discrepancy of the training and test errors, i.e. generalization error, decreases up to , which is the necessary training data size for achieving final performance in our TS-QPI demonstration with deep learning neural network. (b) Comparison of multiple machine learning classification techniques based on the biophysical features extracted from the label-free cell images captured by TS-QPI. Our AUC-based deep learning model (DNN + AUC) has both the highest accuracy and consistency against support vector machine (SVM) with Gaussian kernel, logistic regression (LR), naive Bayes, and conventional deep neural network trained by cross entropy and backpropagation (DNN).