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Example code of basic Convolutional Neural Network (CNN) model in Python, specifically designed for medical imaging in oncology research:

 

```python

import numpy as np

import tensorflow as tf

from tensorflow.keras import layers, models

 

# Load the dataset and preprocess it

# Assuming you have X_train, y_train, X_test, y_test as your dataset

 

# Normalize the images

X_train = X_train.astype('float32') / 255 X_test = X_test.astype('float32') / 255

 

# One-hot encode the labels

num_classes = np.max(y_train) + 1

y_train = tf.keras.utils.to_categorical(y_train, num_classes) y_test = tf.keras.utils.to_categorical(y_test, num_classes)

 

# Build the CNN model

model = models.Sequential()

model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(img_height, img_width, num_channels))) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(128, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2)))

model.add(layers.Flatten())

model.add(layers.Dense(64, activation='relu')) model.add(layers.Dense(num_classes, activation='softmax'))

 

# Compile the model

model.compile(optimizer='adam',

             loss=tf.keras.losses.CategoricalCrossentropy(),

             metrics=['accuracy'])

 

# Train the model

history = model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))

 

# Evaluate the model

test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2) print('Test accuracy:', test_acc) ```

 

Please note that this is a basic template for a CNN model and you may need to customize it further based on the specific requirements of your oncology research project, such as the input image size, number of channels, and the number of classes in your dataset. Additionally, you will need to preprocess your own dataset before training the model.

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