Open Access iconOpen Access

ARTICLE

crossmark

Breast Lesions Detection and Classification via YOLO-Based Fusion Models

Asma Baccouche1,*, Begonya Garcia-Zapirain2, Cristian Castillo Olea2, Adel S. Elmaghraby1

1 Department of Computer Science and Engineering, University of Louisville, Louisville, 40292, KY, USA
2 eVida Research Group, University of Deusto, Bilbao, 4800, Spain

* Corresponding Author: Asma Baccouche. Email: email

(This article belongs to this Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)

Computers, Materials & Continua 2021, 69(1), 1407-1425. https://doi.org/10.32604/cmc.2021.018461

Abstract

With recent breakthroughs in artificial intelligence, the use of deep learning models achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous applications provided efficient solutions to assist radiologists for medical imaging analysis. For instance, automatic lesion detection and classification in mammograms is still considered a crucial task that requires more accurate diagnosis and precise analysis of abnormal lesions. In this paper, we propose an end-to-end system, which is based on You-Only-Look-Once (YOLO) model, to simultaneously localize and classify suspicious breast lesions from entire mammograms. The proposed system first preprocesses the raw images, then recognizes abnormal regions as breast lesions and determines their pathology classification as either mass or calcification. We evaluated the model on two publicly available datasets, with 2907 mammograms from the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and 235 mammograms from INbreast database. We also used a privately collected dataset with 487 mammograms. Furthermore, we suggested a fusion models approach to report more precise detection and accurate classification. Our best results reached a detection accuracy rate of 95.7%, 98.1% and 98% for mass lesions and 74.4%, 71.8% and 73.2% for calcification lesions, respectively on CBIS-DDSM, INbreast and the private dataset.

Keywords


Cite This Article

A. Baccouche, B. Garcia-Zapirain, C. Castillo Olea and A. S. Elmaghraby, "Breast lesions detection and classification via yolo-based fusion models," Computers, Materials & Continua, vol. 69, no.1, pp. 1407–1425, 2021. https://doi.org/10.32604/cmc.2021.018461

Citations




cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 3895

    View

  • 2507

    Download

  • 0

    Like

Share Link