  Convolutional neural networks (CNNs) are one of the most popular models of
Artificial Neural Networks (ANN)s in Computer Vision (CV). A variety of
CNN-based structures were developed by researchers to solve problems like image
classification, object detection, and image similarity measurement. Although
CNNs have shown their value in most cases, they still have a downside: they
easily overfit when there are not enough samples in the dataset. Most medical
image datasets are examples of such a dataset. Additionally, many datasets also
contain both designed features and images, but CNNs can only deal with images
directly. This represents a missed opportunity to leverage additional
information. For this reason, we propose a new structure of CNN-based model:
CompNet, a composite convolutional neural network. This is a specially designed
neural network that accepts combinations of images and designed features as
input in order to leverage all available information. The novelty of this
structure is that it uses learned features from images to weight designed
features in order to gain all information from both images and designed
features. With the use of this structure on classification tasks, the results
indicate that our approach has the capability to significantly reduce
overfitting. Furthermore, we also found several similar approaches proposed by
other researchers that can combine images and designed features. To make
comparison, we first applied those similar approaches on LIDC and compared the
results with the CompNet results, then we applied our CompNet on the datasets
that those similar approaches originally used in their works and compared the
results with the results they proposed in their papers. All these comparison
results showed that our model outperformed those similar approaches on
classification tasks either on LIDC dataset or on their proposed datasets.
