Figure 3 Confusion matrices for the binary (right side) and multiclass (left side) classification between the benign and malignant lymphoid tissues. The x-axis shows the true label. The y-axis corresponds to the predicted label.
As shown in Figure 3, the accuracy is above 95% for every pair of malignant lymphoid tissue and still above 90% for the distinction between benign and malignant lymphoid tissue. The multiclass classification between the lymphoma types (FL and DLBCL) and rLNFC and rLNMZ leads to similarly high accuracy for the binary classification. This multiclass classification’s accuracy is still above 94 % for all neoplastic and reactive lymphoid tissue types.
The neural network, once trained, performs a forward execution of a complex function depicted by the NN, representing the analysis of an IR image in less than 25 ms. NNs with three output classes have been trained to visualize the capabilities of the deep learning approach. These three classes serve as a colour code for an RGB image, whereby the NN classifies the spectrum of each pixel. This means that it was decided to which class the corresponding subsection belongs for each pixel individually. This leads to the satisfactory resolution images shown inFigure 4 A-C .