Deep learning-enhanced prediction of small intestinal bleeding points using long short-term memory networks

Table 2 Convolutional neural network layer configuration and training parameters

Layer type	Details
Input	Capsule endoscopy frame, resized to 224 × 224 pixels, 3 channels (RGB)
Conv block 1	Conv2D, 8 filters, 3 × 3 kernel, ReLU activation → MaxPooling 2 × 2
Conv block 2	Conv2D, 16 filters, 3 × 3 kernel, ReLU activation → MaxPooling 2 × 2
Conv block 3	Conv2D, 32 filters, 3 × 3 kernel, ReLU activation → MaxPooling 2 × 2
Flatten	Flatten feature maps into 1D vector
Fully connected (FC1)	4096 neurons, ReLU activation, dropout = 0.4
Fully connected (FC2)	4096 neurons, ReLU activation
Fully connected (FC3)	1024 neurons, ReLU activation
Fully connected (FC4)	512 neurons, ReLU activation
Fully connected (FC5)	256 neurons, ReLU activation
Output layer	10 neurons, Softmax activation (10-class); 2 neurons, Softmax (2-class)
Training optimizer	Adam, learning rate = 0.001, scheduler (reduce on plateau, factor 0.1)
Batch size	32
Epochs	Up to 180 (early stopping patience = 15)
Loss function	Categorical cross-entropy (10-class); binary cross-entropy (2-class)