DHS‐CapsNet: Dual horizontal squash capsule networks for lung and colon cancer classification from whole slide histopathological images

Abstract

AbstractThis paper proposes a new dual horizontal squash capsule network (DHS‐CapsNet) to classify the lung and colon cancers on histopathological images. DHS‐CapsNet is made up of encoder feature fusion (EFF) and a novel horizontal squash (HSquash) function. The EFF aggregates the extracted feature from the 2‐lane convolutional layers, which provides rich information for better accuracy. HSquash is proposed as a squash function to ensure that vectors are effectively squashed and produces sparsity for a high discriminative capsule to extract important information from images with varied backgrounds. To present the effectiveness of DHS‐CapsNet empirically, we applied this method on histopathological images (LC25000 dataset). We achieved better results of 99.23% compared to traditional CapsNet (85.55%). The DHS‐CapsNet provides the top‐1 classification error of 0.77% compared to 14.45% of the traditional CapsNet. Our results illustrate that our method improves CapsNet and can be adopted as a computer‐aided diagnostic method to support doctors in lung and colon cancer diagnostics.