Table 1 Categorical model summary.

From: Automated spheroid generation, drug application and efficacy screening using a deep learning classification: a feasibility study

Layer (type)

Output shape

Parameter #

conv2d_1 (Conv2D)

(None, 398, 318, 32)

320

activation_1 (Activation)

(None, 398, 318, 32)

0

max_pooling2d_1 (MaxPooling2D)

(None, 199, 159, 32)

0

conv2d_2 (Conv2D)

(None, 197, 157, 64)

18,496

activation_2 (Activation)

(None, 197, 157, 64)

0

max_pooling2d_2 (MaxPooling2D)

(None, 98, 78, 64)

0

conv2d_3 (Conv2D)

(None, 96, 76, 64)

36,928

activation_3 (Activation)

(None, 96, 76, 64)

0

max_pooling2d_3 (MaxPooling2D)

(None, 48, 38, 64)

0

conv2d_4 (Conv2D)

(None, 46, 36, 128)

73,856

activation_4 (Activation)

(None, 46, 36, 128)

0

max_pooling2d_4 (MaxPooling2D)

(None, 23, 18, 128)

0

flatten_1 (Flatten)

(None, 52,992)

0

dense_1 (Dense)

(None, 128)

6,783,104

activation_4 (Activation)

(None, 128)

0

dropout_1 (Dropout)

(None, 128)

0

dense_2 (Dense)

(None, 3)

387

activation_5 (Activation)

(None, 3)

0

Back to article page