What is the importance of neural activation in the application model that can detect tumors?

In the context of data science, neuron activation can play a significant role in terms of introducing non-linearity to the decision boundaries of the model. It would help in enabling it to learn the complex patterns and even relationships within the data. The activation function would also help in controlling the output range of neurons, which would ensure that the prediction of the model should be within a meaningful range.

Here is an example of defining a neural network model using Mera with different activation functions:-

Import tensorflow as tf

From tensorflow.keras.layers import Dense

From tensorflow.keras.models import Sequential

# Define a neural network model with different activation functions

Model = Sequential([

    Dense(units=64, activation=’relu’, input_shape=(input_shape,)),

    Dense(units=64, activation=’sigmoid’),

    Dense(units=1, activation=’softmax’)

])

# Compile the model

Model.compile(optimizer=’adam’, loss=’binary_crossentropy’, metrics=[‘accuracy’])

# Summary of the model architecture

Model.summary()