Assessing the Impact of a Preprocessing Stage on Deep Learning Architectures for Breast Tumor Multi-class Classification with Histopathological Images

Abstract

In this work, we assess the impact of the adaptive unsharp mask filter as a preprocessing stage for breast tumour multi-class classification with histopathological images, evaluating two state-of-the-art architectures, not tested so far for this problem to our knowledge: DenseNet, SqueezeNet and a 5-layer baseline deep learning architecture. SqueezeNet is an efficient architecture, which can be useful in environments with restrictive computational resources. According to the results, the filter improved the accuracy from 2% to 4% in the 5-layer baseline architecture, on the other hand, DenseNet and SqueezeNet show a negative impact, losing from 2% to 6% accuracy. Hence, simpler deep learning architectures can take more advantage of filters than complex architectures, which are able to learn the preprocessing filter implemented. Squeeze net yielded the highest per parameter accuracy, while DenseNet achieved a 96% accuracy, defeating previous state of the art architectures by 1% to 5%, making DenseNet a considerably more efficient architecture for breast tumour classification.