  Convolutional neural networks (CNNs) have shown great promise in improving
computer aided detection (CADe). From classifying tumors found via mammography
as benign or malignant to automated detection of colorectal polyps in CT
colonography, these advances have helped reduce the need for further evaluation
with invasive testing and prevent errors from missed diagnoses by acting as a
second observer in today's fast paced and high volume clinical environment.
CADe methods have become faster and more precise thanks to innovations in deep
learning over the past several years. With advancements such as the inception
module and utilization of residual connections, the approach to designing CNN
architectures has become an art. It is customary to use proven models and fine
tune them for particular tasks given a dataset, often requiring tedious work.
We investigated using a genetic algorithm (GA) to conduct a neural
architectural search (NAS) to generate a novel CNN architecture to find early
stage lung cancer in chest x-rays (CXR). Using a dataset of over twelve
thousand biopsy proven cases of lung cancer, the trained classification model
achieved an accuracy of 97.15% with a PPV of 99.88% and a NPV of 94.81%,
beating models such as Inception-V3 and ResNet-152 while simultaneously
reducing the number of parameters a factor of 4 and 14, respectively.
