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

Figure 1 The process of study: Data collection primarily considers the richness of the data. Dataset construction can achieve data standardization. Neural networks include text vectorization, deep feature mining, and clustering. Data analysis examines the correlation and clustering effects. IM: Intestinal metaplasia.

Figure 2  Diagnostic results generated from magnetically controlled capsule endoscopy images.

Figure 3  Schematic illustration of the dataset construction pipeline.

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.

Figure 5 Correlation analysis of key features in the magnetically controlled capsule endoscopy dataset. Comprehensive correlation analyses were performed to investigate the relationships among multimodal features derived from magnetically controlled capsule endoscopy (MCCE) reports. A: Correlation matrix illustrating pairwise associations between core clinical features of high diagnostic significance and other quantitative/qualitative features extracted from MCCE reports, revealing heterogeneous correlation strengths across feature pairs; B: Heatmap depicting the correlation patterns between clinical manifestations/symptoms and endoscopic features in the MCCE dataset; C: Age-related distribution patterns of gastric diseases, demonstrating distinct correlation patterns across different age groups. GC: Gastric cancer; AG: Atrophic gastritis; GU: Gastric ulcer; GP: Gastric polyps; SG: Erosive gastritis; YWM: Yellow-white macules; VV: Varices; BS: Bleeding and scabs; AGA: Abnormal gastric antrum; OGA: Other gastric abnormalities; BR: Bile reflux; AP: Abdominal pain; AD: Abdominal distension; ADis: Abdominal discomfort; H: Heartburn; AR: Acid regurgitation; GERD: Gastroesophageal reflux; Hem: Bleeding.