HWDCNN: Multi-class recognition in breast histopathology with Haar wavelet decomposed image based convolution neural network

Publication date: Available online 20 September 2019Source: Biocybernetics and Biomedical EngineeringAuthor(s): Tasleem Kausar, MingJiang Wang, Muhammad Idrees, Yun LuAbstractAmong the predominant cancers, breast cancer is one of the main causes of cancer deaths impacting women worldwide. However, breast cancer classification is challenging due to numerous morphological and textural variations that appeared in intra-class images. Also, the direct processing of high resolution histological images is uneconomical in terms of GPU memory. In the present study, we have proposed a new approach for breast histopathological image classification that uses a deep convolution neural network (CNN) with wavelet decomposed images. The original microscopic image patches of 2048 × 1536 × 3 pixels are decomposed into 512 × 384 × 3 using 2-level Haar wavelet and subsequently used in proposed CNN model. The image decomposition step considerably reduces convolution time in deep CNNs and computational resources, without any performance downgrade. The CNN model extracts the deep features from Haar wavelet decomposed images and incorporates multi-scale discriminant features for precise prognostication of class labels. This paper also solves the demand for massive histopathology dataset by means of transfer learning and data augmentation techniques. The efficacy of proposed approach is corroborated on two publicly available breast histology datasets-(a) one provided as a part of ...
Source: Biocybernetics and Biomedical Engineering - Category: Biomedical Engineering Source Type: research