The overall architecture of the Inception-ResNet network.

<p>The overall architecture of the Inception-ResNet network.</p

The number of patients and lesions.

<p>The number of patients and lesions.</p

Some classification results on LIDC-IDRI dataset.

<p>Some classification results on LIDC-IDRI dataset.</p

Compare the multi-view-one-network strategy with the one-view-one-network strategy.

<p>Compare the multi-view-one-network strategy with the one-view-one-network strategy.</p

The result of the ternary classification for the networks with chain architecture.

<p>The result of the ternary classification for the networks with chain architecture.</p

The configuration of the network with chain architecture.

<p>The configuration of the network with chain architecture.</p

CT examples with lung nodules in different categories.

<p>They are benign (left), primary malignant (middle) and metastatic malignant (right), alone with 3 different view areas including 40×40, 50×50 and 60×60.</p

Two kinds of the multi-view strategy.

<p>The one-view-one-network strategy (top) employs a separate network for images from each view (cropping size), while the multi-view-one-network strategy (bottom) uses one network for all views.</p

The validation error rate of the classification for the 3D multi-view networks with DAG architecture.

<p>The validation error rate of the classification for the 3D multi-view networks with DAG architecture.</p

The number of parameters vs. training time and validation error rate for the binary classification (left) and the ternary classification (right).

<p>The area of the circles represents the corresponding validation error rate.</p