Histopathological image classification with deep convolutional neural networks

Md. Zahangir Alom; Theus Aspiras; Tarek M. Taha; Vijayan K. Asari

doi:10.1117/12.2530291

6 September 2019 Histopathological image classification with deep convolutional neural networks

Md. Zahangir Alom, Theus Aspiras, Tarek M. Taha, Vijayan K. Asari

Proceedings Volume 11139, Applications of Machine Learning; 111390X (2019) https://doi.org/10.1117/12.2530291
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

In the last few years, deep learning approaches have been applied successfully in different modalities of medical imaging problems and achieved state-of-the-art accuracy. Due to the huge volume and variety of imaging modalities, it remains a large open research area. However, in this paper, we have applied Inception Residual Recurrent Convolutional Neural Network (IRRCNN) model for histopathological image classification where a new publicly available dataset named KIMIA Path960 is used. This database contains 960 histopathological images with 20 different classes (different types of tissue collected from 400 Whole Slide Images). In this implementation, we have evaluated the model with non-overlapping patches size of 64×64 pixels and the variant of samples are generated from each patch with different data augmentation techniques including rotation, shear, zooming, and horizontal and vertical flipping. The experimental results are compared against Linear Binary Pattern (LBP), bag-of-visual words (BoVW), and deep learning method with AlexNet and VGG16 networks. The IRRCNN model shows around 98.79% testing accuracy for augmented patch-level evaluation which is around 2.29% and 4% superior performance compared to Support Vector Machine with histogram intersection kernel (IKSVM) with BoVW and VGG16 methods respectively. Additionally, this evaluation also demonstrates that the deep feature representation-based method outperforms compared to a traditional feature-based method including LBP and BoVW for the histopathological image classification problem.

Citation Download Citation

Md. Zahangir Alom, Theus Aspiras, Tarek M. Taha, and Vijayan K. Asari "Histopathological image classification with deep convolutional neural networks", Proc. SPIE 11139, Applications of Machine Learning, 111390X (6 September 2019); https://doi.org/10.1117/12.2530291

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