2020
DOI: 10.1016/j.asoc.2020.106759
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Deep transfer with minority data augmentation for imbalanced breast cancer dataset

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Cited by 102 publications
(39 citation statements)
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“…Bi-directional Long-Short Term Memory model [ 162 ] approach was also proposed for the classification using context-based patch modeling. A deep transfer network [ 130 ] using Deep Convolutional Generative Adversarial Network (DCGAN) as a data augmentation technique was proposed to tackle the data imbalance problem.…”
Section: Image Processing Approachesmentioning
confidence: 99%
“…Bi-directional Long-Short Term Memory model [ 162 ] approach was also proposed for the classification using context-based patch modeling. A deep transfer network [ 130 ] using Deep Convolutional Generative Adversarial Network (DCGAN) as a data augmentation technique was proposed to tackle the data imbalance problem.…”
Section: Image Processing Approachesmentioning
confidence: 99%
“…The algorithm was classified with an average accuracy of 89.23% for disease classification. Saini et al [15] The class imbalanced distribution results in a deterioration in the efficiency of the classification models owing to a bias in the category toward this dominant class to tackle this issue. The author has suggested a different learning technique that includes a deep transfer network in conjunction with the deep convolution generative adversarial network.…”
Section: Literature Surveymentioning
confidence: 99%
“…It refers to the datasets with skewed classes when dealing with multiple disease classes. With class-imbalanced datasets, deep neural networks train better on the classes with a large number of images rather than the class with a limited number of images [8]. Data augmentation is one of the potential solutions to address the class imbalance, as well as data limitation problems [9].…”
Section: Introductionmentioning
confidence: 99%