Computer Vision News - March 2023

27 with DenseNet Neural Network Traditionally, medical image segmentation has been a labor- intensive process that involves manually delineating the boundaries of a tumor in each image slice. However, with the development of deep learning models like DenseNet, we can now automate this process to a considerable extent. So, let us have a look at doing this for a CT scan! Now for a change, I will use TensorFlow and Keras and I will let you implement the final two steps. Feel free to send your results and/or questions to my email or social media! Happy to hear from you: import tensorflow as tf from tensorflow.keras import layers from tensorflow.keras.models import Model from tensorflow.keras.applications import DenseNet121 # Load pretrained Densenet model densenet = DenseNet121(weights='imagenet', include_top=False, input_shape=(224, 224, 3)) # Add segmentation head x = layers.GlobalAveragePooling2D()(densenet.output) x = layers.Dense(256, activation='relu')(x) x = layers.Dropout(0.5)(x) x = layers.Dense(1, activation='sigmoid')(x) # Build the model model = Model(inputs=densenet.input, outputs=x) # Compile the model model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # Load CT scan data and train the model # exercise for you! # Use the model for tumor segmentation # Same here, please email me your results!