2021
DOI: 10.46792/fuoyejet.v6i2.617
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Breast Cancer Classification Using Deep Convolutional Neural Networks

Abstract: Breast cancer remains the primary causes of death for women and much effort has been depleted in the form of screening series for prevention. Given the exponential growth in the number of mammograms collected, computer-assisted diagnosis has become a necessity. Histopathological imaging is one of the methods for cancer diagnosis where Pathologists examine tissue cells under different microscopic standards but disagree on the final decision. In this context, the use of automatic image processing techniques resu… Show more

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Cited by 9 publications
(2 citation statements)
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“…Boumaraf et al [11] utilize the transfer learning combined with the well-known VGG-19 model, demonstrating the superior performance of deep learning based methods on this medical task. In addition, Chukwu et al [24] fine-tune the pre-trained DenseNet network for breast cancer histopathology image classification, and they gain the highest accuracy of 97.42% on the BreakHis dataset. Instead of using a single backbone, Kallipolitis et al [23] integrate three different pre-trained EfficientNets to achieve better classification performance for this medical task.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Boumaraf et al [11] utilize the transfer learning combined with the well-known VGG-19 model, demonstrating the superior performance of deep learning based methods on this medical task. In addition, Chukwu et al [24] fine-tune the pre-trained DenseNet network for breast cancer histopathology image classification, and they gain the highest accuracy of 97.42% on the BreakHis dataset. Instead of using a single backbone, Kallipolitis et al [23] integrate three different pre-trained EfficientNets to achieve better classification performance for this medical task.…”
Section: Introductionmentioning
confidence: 99%
“…Next, other works directly utilize trainable CNNs for breast cancer histopathology image classification, which can be further divided into two sub-classes. The first thought is to employ classic pre-trained networks with the fine-tuning strategy, called task-specific CNN methods [9,11,[22][23][24]. For example, Zhang et al [9] adopt VGGNet and ResNet models to classify breast cancer histopathology images and explore the effects of data enhancement.…”
Section: Introductionmentioning
confidence: 99%