Research Article
SchizoGoogLeNet: The GoogLeNet-Based Deep Feature Extraction Design for Automatic Detection of Schizophrenia
Table 1
Model architecture of GoogLeNet used in this study.
| Layer | Patch size/stride | Depth | #1 × 1 | #3 × 3 reduce | #3 × 3 | #5 × 5 reduce | #5 × 5 | Pool proj | Output size |
| Conv1 | 7 × 7/2 | 1 | | | | | | | 112 × 112 × 64 | Max pool1 | 3 × 3/2 | 0 | | | | | | | 56 × 56 × 64 | Conv2 | 3 × 3/1 | 2 | | 64 | 192 | | | | 56 × 56 × 192 | Max pool2 | 3 × 3/2 | 0 | | | | | | | 28 × 28 × 192 | Inception-3a | | 2 | 64 | 96 | 128 | 16 | 32 | 32 | 28 × 28 × 256 | Inception-3b | | 2 | 128 | 128 | 192 | 32 | 96 | 64 | 28 × 28 × 480 | Max pool3 | 3 × 3/2 | 0 | | | | | | | 14 × 14 × 480 | Inception-4a | | 2 | 192 | 96 | 208 | 16 | 48 | 64 | 14 × 14 × 512 | Inception-4b | | 2 | 160 | 112 | 224 | 24 | 64 | 64 | 14 × 14 × 512 | Inception-4c | | 2 | 128 | 128 | 256 | 24 | 64 | 64 | 14 × 14 × 512 | Inception-4d | | 2 | 112 | 144 | 288 | 32 | 64 | 64 | 14 × 14 × 528 | Inception-4e | | 2 | 256 | 160 | 320 | 32 | 128 | 128 | 14 × 14 × 832 | Max pool4 | 3 × 3/2 | 0 | | | | | | | 7 × 7 × 832 | Inception-5a | | 2 | 256 | 160 | 320 | 32 | 128 | 128 | 7 × 7 × 832 | Inception-5b | | 2 | 384 | 192 | 384 | 48 | 128 | 128 | 7 × 7 × 1024 | Average pool5 | 7 × 7/1 | 0 | | | | | | | 1 × 1 × 1024 | Dropout (40%) | | 0 | | | | | | | 1 × 1 × 1024 | Fc | | 1 | | | | | | | 1 × 1 × 2 | Softmax | | 0 | | | | | | | 1 × 1 × 2 | Classification output | | | | | | | | | 1 × 1 × 2 |
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