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. 2022 Aug 13;14(16):3914. doi: 10.3390/cancers14163914

Table 15.

Architecture of deep learning models.

Model Hyperparameters
LSTM Embedding (4000, 100, input_length = …)
Dropout (0.5)
LSTM (128)
Dense (5, activation = ‘softmax’)
CNN Embedding (4000, 100, input_length = …)
Conv1D (128, 5, activation = ‘relu’)
MaxPooling1D (pool_size = 5)
Activation (‘relu’)
Dropout (rate = 0.5)
Flatten()
Dense (5, activation = ‘softmax’)
CNN-LSTM Embedding (4000, 100, input_length = …)
Conv1D (128, 5, activation = ‘relu’)
MaxPooling1D (pool_size = 5)
LSTM (100)
Dense (5, activation = ‘softmax’)
loss = ‘categorical_crossentropy’, optimizer = ‘adam’,
epochs = 100, batch_size = 16