cnn_model.py

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

def build_cnn(input_shape):
    """
    Build a Convolutional Neural Network (CNN) for medical imaging.
    
    Parameters:
        input_shape (tuple): The shape of input images.
        
    Returns:
        Sequential: A simple CNN model.
    """
    model = Sequential()
    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))
    
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    return model