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A Deep Learning Model for Mammography Mass Detection Using Mosaic and Reconstructed Multichannel Images

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Computational Science and Its Applications – ICCSA 2022 (ICCSA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13375))

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Abstract

Breast cancer is the most publicized cancer that hits women around the world. It’s considered as the second cause of death among females. Early detection helps a lot in increasing the survival rate, and the probability of recovery from this disease. The mammogram is the main screening modality that is used regularly for breast cancer diagnosis. The accurate interpretation of the mammogram is very important for mass detection and diagnosis. The rapid evolution of deep learning is contributing to introduce more accurate systems that can act as a second opinion for the radiologists, and accordingly, this can help in providing an accurate diagnosis. In this paper, we propose a model for mass detection and classification based on You Look Only Once (YOLO)v4. We designed the experiment to investigate the performance of different augmentation techniques using YOLOv4 including mosaic that was introduced by YOLOv4. Furthermore, in the preprocessing phase, the images were reconstructed to be in a multichannel format which enhanced the detection accuracy by almost \(\simeq\) 10%. The model was evaluated with the usage of different combinations of augmentation techniques (mosaic, mix-up, and conventional augmentation). The experiments were conducted on the INbreast and MIAS datasets, the results of INbreast showed that mosaic with YOLOv4 achieved the best results with mAP (mean average precision), precession, and recall of almost \(\simeq\) 99.5%, 98%, and 94% respectively for detection. In addition, the proposed model achieved AP of 99.16% and 99.58% for classifying the detected masses as benign and malignant respectively. Furthermore, the best results on MIAS achieved mAP, precession, and recall of 95.28%, 93%, and 90% respectively. Finally, our methodology showed competitive performance compared to other similar studies.

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Author information

Authors and Affiliations

  1. College of Computing and Information Technology, Arab Academy for Science and Technology, Cairo, Egypt

    Nada M. Hassan

  2. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Safwat Hamad

  3. College of Computing and Information Technology, Arab Academy for Science and Technology, Alexandria, Egypt

    Khaled Mahar

Authors
  1. Nada M. Hassan
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  2. Safwat Hamad
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  3. Khaled Mahar
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Corresponding author

Correspondence to Nada M. Hassan .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Universidad de Málaga, Malaga, Spain

    Eligius M. T. Hendrix

  4. Monash University, Clayton, VIC, Australia

    David Taniar

  5. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

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Hassan, N.M., Hamad, S., Mahar, K. (2022). A Deep Learning Model for Mammography Mass Detection Using Mosaic and Reconstructed Multichannel Images. In: Gervasi, O., Murgante, B., Hendrix, E.M.T., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2022. ICCSA 2022. Lecture Notes in Computer Science, vol 13375. Springer, Cham. https://doi.org/10.1007/978-3-031-10522-7_37

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  • DOI: https://doi.org/10.1007/978-3-031-10522-7_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10521-0

  • Online ISBN: 978-3-031-10522-7

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