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

Kausar, Tasleem; Wang, Mingjiang; Idrees, Muhammad; Lu, Yun

doi:10.1016/j.bbe.2019.09.003

Cited by 56 publications

(49 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [113], a DCNN model with Haar wavelet decomposed images is introduced to classify breast histopathological images. Haar wavelet transform is used to decompose the input high-resolution histopathological image to a small size.…”

Section: ) ''Bach'' Tasksmentioning

confidence: 99%

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

et al. 2020

View full text Add to dashboard Cite

Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.

show abstract

Section: ) ''Bach'' Tasksmentioning

confidence: 99%

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The patch based methods do not consider the truncated cell as a valid cell so fails to recognize several mitotic events. Instead of using the patch extraction strategies, the SmallMitosis detector is trained and tested on wavelet decomposed images [14,15]. Since detection performance also depends on the scale of input HPF image, therefore the impact of input image size on detection accuracy is empirically checked.…”

Section: A New Atrous Convolution Based Annotation (A-fcn)mentioning

confidence: 99%

“…Recently, Sabeena et al [35] proposed a transfer learning based approach, in which the weights of pre-trained model are transferred to the ICPR 2014 dataset. In [15], we performed multi-class breast cancer recognition using Haar wavelet decomposed convolution neural network (HWDCNN). The Haar wavelet decomposed images are subjected to the HWDCNN model and its performance is tested on ICIAR 2018 breast histology image dataset.…”

Section: Related Workmentioning

confidence: 99%

“…In Experiment 2, original HPF images are downsampled to 521×521 pixel images by simple bilinear interpolation. In Experiment 3, instead of going to patch extraction or downsampling, original high resolution images are decomposed to small size using 2-level Haar wavelet decomposition method [14,15]. The average, precision, Recall, and F score of MS-RCNN detectors trained on different size images, are given in Table 4.…”

Section: ) the Effectiveness Of Input Image Size And Super-resolved mentioning

confidence: 99%

See 1 more Smart Citation

SmallMitosis: Small Size Mitotic Cells Detection in Breast Histopathology Images

et al. 2021

Self Cite

View full text Add to dashboard Cite

“…In Reference 18, the researchers demonstrated the use of transfer learning‐based pretrained VGG16, VGG19, and ResNet50 to classify the breast cancer histopathology dataset. In Reference 19, the authors used the BreakHis dataset images transformed by the Haar wavelet as input to the pretrained VGG16 20 for feature extraction and classification. The aforementioned studies 12,14,15,17,19 used no technique to deal with minority class instances.…”

Section: Introductionmentioning

confidence: 99%

Breast cancer histopathology image classification using kernelized weighted extreme learning machine

Saxena

Shukla

Gyanchandani

2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Histopathology is considered as the gold standard for diagnosing breast cancer. Traditional machine learning (ML) algorithm provides a promising performance for cancer diagnosis if the training dataset is balanced. Nevertheless, if the training dataset is imbalanced the performance of the ML model is skewed toward the majority class. It may pose a problem for the pathologist because if the benign sample is misclassified as malignant, then a pathologist could make a misjudgment about the diagnosis. A limited investigation has been done in literature for solving the class imbalance problem in computer‐aided diagnosis (CAD) of breast cancer using histopathology. This work proposes a hybrid ML model to solve the class imbalance problem. The proposed model employs pretrained ResNet50 and the kernelized weighted extreme learning machine for CAD of breast cancer using histopathology. The breast cancer histopathological images are obtained from publicly available BreakHis and BisQue datasets. The proposed method achieved a reasonable performance for the classification of the minority as well as the majority class instances. In comparison, the proposed approach outperforms the state‐of‐the‐art ML models implemented in previous studies using the same training‐testing folds of the publicly accessible BreakHis dataset.

show abstract

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

Cited by 56 publications

References 29 publications

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

SmallMitosis: Small Size Mitotic Cells Detection in Breast Histopathology Images

Breast cancer histopathology image classification using kernelized weighted extreme learning machine

Contact Info

Product

Resources

About