Hyper-Tuned CNN Using EVO Technique for Efficient Biomedical Image Classification

Table 4

Parameters for CNN.

Model	Sequential
CL 1	FS = 256, KS = (3, 3) Padding = same, AF = ReLu, IS = (75, 75, 1)
Max pool	PS = (3,3), dropout = 0.3
CL 2	FS = 128, KS = (3, 3) Padding = same, AF = ReLU
Max pool	PS = (2, 2), dropout = 0.3
CL 3	FS = 256, KS = (2, 2) Padding = same, AF = ReLU
Max pool	PS = (2, 2), dropout = 0.3; Dense = 824, AF = ReLU
Optimizer	Adam, lr = 0.01, beta_1 = 0.7, beta_2 = 0.79
Epoch, batch size	Epoch = 50, batch size = 50