Deep self-organizing competitive neural network for magnetically controlled capsule endoscopy-based auxiliary diagnosis of clinically significant gastric lesions

Figure 4 Framework of the deep self-organizing competitive neural network. This figure presents the end-to-end architecture and operational principles of the proposed deep self-organizing competitive neural network for clinical feature and magnetically controlled capsule endoscopy report analysis. A: Overview of the deep self-organizing competitive neural network workflow, illustrating the integration of feature extraction, competitive learning, and diagnostic prediction, along with the selected network architecture and key components; B: Detailed feature extraction process via text convolutional neural network, including text vectorization, multi-kernel convolution, rectified linear unit activation, and average pooling to capture hierarchical clinical semantic features; C: Schematic of the self-organizing competitive clustering mechanism, where neurons compete for input patterns to learn topological feature distributions and enable unsupervised clustering of high-dimensional data for disease classification. TextCNN: Text convolutional neural network; GloVe: Global vectors for word representation.