Open Access

ARTICLE

Breast Lesions Detection and Classification via YOLO-Based Fusion Models

Asma Baccouche^1,*, 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:

(This article belongs to the 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

Received 09 March 2021; Accepted 12 April 2021; Issue published 04 June 2021

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.

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