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

Table 1 Details of the dataset used in the study

Kvasir labeled video
Category		10
Bleeding images		1312 frames
Else		6081 frames
Total		7393 frames
Train	Stage: Bleeding	Images 1049	Total = 5913
	Stage: Un-bleeding	Images 4864
Val	Stage: Bleeding	Images 263	Total = 1480
	Stage: Un-bleeding	Images 1217

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)

Table 3 Summary of metric

Model	Precision	Recall	F1-score	Accuracy
10-class (collapsed)	98.6%	93.3%	95.9%	93.3%
2-class (direct)	98.4%	95.1%	96.7%	94.7%