Abstract
The processing of medical imaging studies is a costly and error-prone task. The use of deep learning algorithms for the automated classification of abnormalities can aid radiologists in interpreting medical images. This paper presents a genetic algorithm that is used to fine-tune the internal parameters of convolutional neural networks trained for abnormality classification in mammographic imaging. We used our genetic algorithm to search for the neural network weights representing the global minimum solution for ResNet50 and Xception architectures. The Xception architecture outperformed the ResNet baseline for both tasks, with the Xception baseline model achieving an AUC score of 72%. The genetic algorithm demonstrated a slight proclivity for improving the general metric evaluations of the network that it fine-tuned, but in some cases, it was still prone to miss good regions in the search space.