Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution

Pak, Fatemeh; Kanan, Hamidreza Rashidy; Alikhassi, Afsaneh

doi:10.1016/j.cmpb.2015.06.009

Cited by 57 publications

(22 citation statements)

References 43 publications

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“…Previous studies [18,22,36] have shown that combining texture feature with multiresolution transform domain feature can help improving the classification accuracy. In this work, feature is extracted from the multiresolution domain based on ROI.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Orozco et al [21] presented a CAD system to distinguish lung nodules CT images based on supervised extraction of the ROI; experimental results showed that this method helped reducing the complexity of classification without the segmentation stage. Pak et al [22] used ROI-feature extraction based on Nonsubsampled Contourlet Transform (NSCT) and Super Resolution (SR); then AdaBoost algorithm was used to classify and determine the probability of benign and malignant. Beura et al [23] employed Gray Level Cooccurrence Matrix (GLCM) to all the detailed wavelet coefficients based on ROI and then classified the breast tissues as normal, benign, or malignant using Back Propagation Neural Network (BPNN).…”

Section: Introductionmentioning

confidence: 99%

“…These classification techniques can be divided into two categories. One is image analysis with segmentation the lesion areas [10][11][12][13][14][15][16], and the other is image analysis without segmentation [17][18][19][20][21][22][23]. Wei et al [10] come up with a content-based mammogram retrieval system; meanwhile, a similarity measure scheme was proposed, this study was tested on Digital Database for Screening Mammography (DDSM) dataset, and experimental results demonstrated that round-shape masses were most discriminative when using Zernike moments and roundshape, circumscribed margin masses could achieve the highest precision among all mass types.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN

Dong¹,

Wang²,

Cheng-hui³

et al. 2017

Journal of Sensors

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Goal. Breast cancer is becoming one of the most common cancers among women. Early detection can help increase the survival rates. Feature extraction directly affects diagnosis result. In this work, a novel feature extraction method based on Dual Contourlet Transform (Dual-CT) is presented, and improved nearest neighbor (KNN) is employed to improve the classification performance. Method. This presented method includes three main sections: firstly, the Region of Interest (ROI) is cropped manually according to gold standard from Mammographic Image Analysis Society (MIAS) database; secondly, the ROIs are decomposed into different resolution levels using Dual-CT, contourlet, and wavelet; a set of texture features are extracted. Then improved KNN and traditional KNN are implemented for classification. Experiments are performed on 324 ROIs which include 206 normal cases and 118 abnormal cases; the abnormal cases are composed of 66 benign cases and 52 malignant cases. Results. Experimental results prove the validity and superiority of Dual-CT-based feature and improved KNN. In particular, 94.14% and 95.76% classification accuracy is achieved based on Dual-CT domain. Moreover, the proposed method is comparable with state-of-the-art methods in terms of accuracy. Contribution. Dual-CT-based feature is used for analyzing mammogram and can help improve breast cancer diagnosis accuracy.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN

Dong¹,

Wang²,

Cheng-hui³

et al. 2017

Journal of Sensors

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show abstract

“…The non-subsampled CT transform has been utilized for Breast mass classification by Leena Jasmine along with the SVM techniques [32]. Pak et al also utilized Non-subsampled CT for breast-image (MIAS dataset) classification and obtained 91.43% mean Accuracy and 6.42% mean False Positive Rate (FPR) [33].…”

Section: Introductionmentioning

confidence: 99%

Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

Nahid

Kong

2018

Information

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Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes experts disagree on their decisions. However, Computer Aided Diagnosis (CAD) techniques help the radiologist to give a second opinion that can increase the reliability of the radiologist's decision. Among the different image analysis techniques, classification of the images has always been a challenging task. Due to the intense complexity of biomedical images, it is always very challenging to provide a reliable decision about an image. The state-of-the-art Convolutional Neural Network (CNN) technique has had great success in natural image classification. Utilizing advanced engineering techniques along with the CNN, in this paper, we have classified a set of Histopathological Breast-Cancer (BC) images utilizing a state-of-the-art CNN model containing a residual block. Conventional CNN operation takes raw images as input and extracts the global features; however, the object oriented local features also contain significant information-for example, the Local Binary Pattern (

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“…The performance of the technique was rated to outperform the existing UWB Radar imaging algorithm. In their work Fatemeh et al [19] proposed an algorithm based on NonSubsampled Contourlet Transform and Super Resolution to improve the quality of digital mammography images. The authors then used AdaBoost algorithm to classify and determine the probability of a disease being a benign and malign cancer.…”

Section: Introductionmentioning

confidence: 99%

Prediction of breast cancer survivability using ensemble algorithms

Adegoke

Chen

Banissi

et al. 2017

2017 International Conference on Smart Systems and Technologies (SST)

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-In this paper, several ensemble cancer survivability predictive models are presented and tested based on three variants of AdaBoost algorithm. In the models we used Random Forest, Radial Basis Function Network and Neural Network algorithms as base learners while AdaBoostM1, Real AdaBoost and MultiBoostAB were used as ensemble techniques and ten other classifiers as standalone models. There has been major research in ensemble modelling in statistics, medicine, technology and artificial intelligence in the last three decades. This might be because of the effectiveness and reliability of the technique in medical diagnosis and incident predictions compare with the standalone classifiers.We used Wisconsin breast cancer dataset in training and testing the models. The performances of the ensemble and standalone models were evaluated using Accuracy, RMSE and confusion matrix predictive parameters. The result shows that despite the complexity of the ensemble models and the required training time, the models did not outperform most of the standalone classifiers.

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Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution

Cited by 57 publications

References 43 publications

Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN

Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN

Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

Prediction of breast cancer survivability using ensemble algorithms

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