Ensemble CNNs for Breast Tumor Classification

Muhammad Umar Farooq, Zahid Ullah, Jeonghwan Gwak
4/11/2023
eess.IVcs.AIcs.CV

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Abstract

To improve the recognition ability of computer-aided breast mass classification among mammographic images, in this work we explore the state-of-the-art classification networks to develop an ensemble mechanism. First, the regions of interest (ROIs) are obtained from the original dataset, and then three models, i.e., XceptionNet, DenseNet, and EfficientNet, are trained individually. After training, we ensemble the mechanism by summing the probabilities outputted from each network which enhances the performance up to 5%. The scheme has been validated on a public dataset and we achieved accuracy, precision, and recall 88%, 85%, and 76% respectively.

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Cite this paper

@article{farooq2023ensemble,
  title  = {Ensemble CNNs for Breast Tumor Classification},
  author = {Muhammad Umar Farooq and Zahid Ullah and Jeonghwan Gwak},
  year   = {2023},
  eprint = {2304.13727},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.13727v1}
}

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