Wyniki wyszukiwania - BazTech

Ograniczanie wyników

1 Biocybernetics and Biomedical Engineering

1 2019

Powiadomienia systemowe

Sesja wygasła!
Sesja wygasła!

Znaleziono wyników: 1

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

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

Kausar Tasleem, Wang MingJiang, Idrees Muhammad, Lu Yun

Biocybernetics and Biomedical Engineering

2019

Vol. 39, no. 4

967--982

Among 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 international conference on image analysis and recognition (ICIAR 2018) grand challenge and (b) on BreakHis data. On the ICIAR 2018 validation data, our model showed an accuracy of 98.2% for both 4-class and 2-class recognition. Further, on hidden test data of the ICIAR 2018, we achieved an accuracy of 91%, outperforming existing state-of-the-art results significantly. Besides, on BreakHis dataset, the model achieved competing performance with 96.85% multi-class accuracy.