Self-AttentionNeXt: Exploring schizophrenic optical coherence tomography image detection investigations

Figure 1 Publications featuring the key terms “optical coherence tomography”, “artificial intelligence”, and “schizophrenia”. A: Optical coherence tomography (OCT)-based artificial intelligence papers; B: OCT-based automated schizophrenia detection papers. WOS: Web of Science.

Figure 2 Proposed Self-AttentionNeXt block. 1 × 1, 3 × 3, 7 × 7: Convolutional kernel sizes; BN: Batch normalization; Concat: Depth concatenation; F: Number of filters; LR: Leaky ReLu.

Figure 3  Sample images from the collected dataset.

Figure 4 Block diagram of the proposed Self-AttentionNeXt. Dropout layer was used to mitigate overfitting. GAP: Global average pooling.

Figure 5 Detailed specification of the recommended Self-AttentionNeXt. BN: Batch normalization.

Figure 6 Training and validation curves of the collected dataset and the Mendeley dataset. A: From left to right case of our collected dataset. Training accuracy: 100%, validation accuracy: 8.03%; B: From top to bottom case of our collected dataset. Training accuracy: 100%, validation accuracy: 7.75%; C: Mendeley dataset (OCT2017). Training accuracy: 98.44%, validation accuracy: 89.85%.

Figure 7 Confusion matrices and receiver operating curve for the collected dataset. A: Confusion matrices of from left to right; B: Confusion matrices of from top to bottom; C: Receiver operating curve of from left to right; D: Receiver operating curve of from top to bottom. 1: Healthy; 2: Schizophrenia.

Figure 8 Confusion matrix and receiver operating curve for the Mendeley dataset. CNV: Choroidal neovascularization; DME: Diabetic macular edema.

Figure 9 Heatmaps of the sample optical coherence tomography images. OCT: Optical coherence tomography; SZ: Schizophrenia.

Figure 10  Comparison with well-known convolutional neural networks. CNN: Convolutional neural network.

Figure 11  Classification accuracies of the utilized cases on the utilized both datasets.