Research Article
Automated Detection of Infection in Diabetic Foot Ulcer Images Using Convolutional Neural Network
Table 2
DFINET layer parameter details.
| | Layer type | Layer parameters Kernel size (KS), number of filters (NF) |
| | Conv_1 | KS = 7 × 7, NF = 64 | | Max_pool_1 | KS = 3 × 3, Stride = 2 | | Conv_2 | KS = 3 × 3, NF = 64 | | Conv_3 | KS = 3 × 3, NF = 128 | | Max_pool_2 | KS = 3 × 3, Stride = 2 | | Conv_4a | KS = 3 × 3, NF = 128 | | Conv_4b | KS = 1 × 1, NF = 128 | | Max_pool_3 | KS = 3 × 3, Stride = 2 | | Conv_5a | KS = 3 × 3, NF = 128 | | Conv_5b | KS = 1 × 1, NF = 128 | | Conv_6a | KS = 3 × 3, NF = 256 | | Conv_6b | KS = 1 × 1, NF = 256 | | Max_pool_4 | KS = 3 × 3, Stride = 2 | | Conv_7a | KS = 3 × 3, NF = 256 | | Conv_7b | KS = 1 × 1, NF = 256 | | Max_pool_5 | KS = 7 × 7, Stride = 2 | | FC_1 | 100 | | Dropout | Probability = 0.3 | | FC_1 | 2 | | Total number of parameters | 14, 895, 440 |
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