Evolution of Convolutional Neural Networks for Lymphoma Classification

There are more than 60 subtypes of Lymphoma. This diversity usually requires a specialised pathologist for diagnosis. We aimed to investigate the effectiveness of Artificial Neural Networks (ANNs) and Deep Learning at Lymphoma classification. We also sought to determine whether Evolutionary Algorithms (EAs) could optimise accuracy. Tensorflow and Keras were used for network construction, and we developed a novel framework to evolve their weights. The best network was a Convolutional Neural Network (CNN); its 10-fold cross-validation test accuracy after training and weight evolution was 95.64%. The best single run test accuracy was 98.41%. This suggests that ANNs can classify Lymphoma biopsies at a test accuracy higher than the average human pathologist. The EA consistently improved accuracy, demonstrating that they are a useful technique to optimise ANNs for Lymphoma classification.