Research Article
COVID-19 and Pneumonia Diagnosis in X-Ray Images Using Convolutional Neural Networks
Table 3
The proposed model architecture.
| | Layer (type) | Output shape | Parameters # |
| | Conv2d_2 (conv2d) | (180, 180, 16) | 448 | | Conv2d_3 (conv2d) | (180, 180, 16) | 2320 | | Max_pooling2d_1 | (90, 90, 16) | 0 | | Sequential (sequential) | (45, 45, 32) | 2160 | | Sequential_1 (sequential) | (22, 22, 64) | 7392 | | Sequential_2 (sequential) | (11, 11, 128) | 27072 | | Dropout (dropout) | (11, 11, 128) | 0 | | Sequential_3 (sequential) | (5, 5, 256) | 103296 | | Dropout_1 (dropout) | (5, 5, 256) | 0 | | Flatten (flatten) | (6400) | 0 | | Sequential_4 (sequential) | (512) | 3279360 | | Sequential_5 (sequential) | (128) | 66176 | | Sequential_6 (sequential) | (64) | 8512 | | Dense_7 (dense) | (512) | 33280 | | Dense_8 (dense) | (3) | 1539 |
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