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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2304.13727 (eess)
[Submitted on 11 Apr 2023]

Title:Ensemble CNNs for Breast Tumor Classification

Authors:Muhammad Umar Farooq (1), Zahid Ullah (1), Jeonghwan Gwak (1) ((1) Korea National University of Transportation)
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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.
Comments: SMA 2021: The 10th International Conference on Smart Media and Applications Gunsan Saemangeum Convention Center and Kunsan National University Gunsan-si, South Korea, September 9-11, 2021
Subjects: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2304.13727 [eess.IV]
  (or arXiv:2304.13727v1 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2304.13727

Submission history

From: Muhammad Umar Farooq [view email]
[v1] Tue, 11 Apr 2023 10:59:38 UTC (565 KB)
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