Deep learning-enhanced prediction of small intestinal bleeding points using long short-term memory networks

Figure 6 Confusion matrices comparing 10-class and 2-class convolutional neural network performance for capsule endoscopy bleeding detection. (Top) Original 10-class confusion matrix (left) and the corresponding collapsed 2-class version (right), where bleeding-related categories (classes 0-1) were grouped as “bleeding” and classes 2-9 were grouped as “non-bleeding.” (Bottom left) Collapsed 10-class confusion matrix showing classification counts (true positive = 228, false negative = 16, false positive = 83, true negative = 1151). (Bottom middle and right) Normalized confusion matrices for the 10-class convolutional neural network (CNN) (converted to 2-class) and the direct 2-class CNN, respectively. Results indicate nearly identical performance between the collapsed and direct 2-class models, with high sensitivity and specificity in both cases. CNN: Convolutional neural network.