CNN architecture optimization using bio-inspired algorithms for breast cancer detection in infrared images

Abstract

The early detection of breast cancer is a vital factor when it comes to improving cure and recovery rates in patients. Among such early detection factors, one finds thermography, an imaging technique that demonstrates good potential as an early detection method. Convolutional neural networks (CNNs) are widely used in image classification tasks, but finding good hyperparameters and architectures for these is not a simple task. In this study, we use two bio-inspired optimization techniques, genetic algorithm and particle swarm optimization to find good hyperparameters and architectures for the fully connected layers of three state of the art CNNs: VGG-16, ResNet-50 and DenseNet-201. Through use of optimization techniques, we obtained F1-score results above 0.90 for all three networks, an improvement from 0.66 of the F1-score to 0.92 of the F1-score for the VGG-16. Moreover, we were also able to improve the ResNet-50 from 0.83 of the F1-score to 0.90 of the F1-score for the test data, when compared to previously published studies.