Histopathological Image Classification with Deep Convolutional Neural Networks

摘要

In the last few years, deep learning approaches have been applied successfully in different modalities of medical imaging problemsand achieved state-of-the-art accuracy. Due to the huge volume and variety of imaging modalities, it remains a large open researcharea. However, in this paper, we have applied Inception Residual Recurrent Convolutional Neural Network (IRRCNN) model forhistopathological image classification where a new publicly available dataset named KIMIA Path960 is used. This database contains960 histopathological images with 20 different classes (different types of tissue collected from 400 Whole Slide Images). In thisimplementation, we have evaluated the model with non-overlapping patches size of 64×64 pixels and the variant of samples aregenerated from each patch with different data augmentation techniques including rotation, shear, zooming, and horizontal and verticalflipping. The experimental results are compared against Linear Binary Pattern (LBP), bag-of-visual words (BoVW), and deep learningmethod with AlexNet and VGG16 networks. The IRRCNN model shows around 98.79% testing accuracy for augmented patch-levelevaluation which is around 2.29% and 4% superior performance compared to Support Vector Machine with histogram intersectionkernel (IKSVM) with BoVW and VGG16 methods respectively. Additionally, this evaluation also demonstrates that the deep featurerepresentation-based method outperforms compared to a traditional feature-based method including LBP and BoVW for thehistopathological image classification problem.