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Detection of Breast Tumor in Mammograms Using Single Shot Detector Algorithm

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Advances in Computing and Data Sciences (ICACDS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1614))

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Abstract

The influence of AI over Healthcare is increasing day by day. Lot of efforts are being made to increase the efficiency of Cancer diagnosis at the early stages, where medical images play a vital role. Among the women, Breast cancer is on a steep rise with an alarming statistics of, Indian woman being diagnosed with Breast cancer, for every four minutes. Though Breast cancer is a curable disease, identifying the problem early is an important metric for the successful outcome. Earlier days, Mammograms were the only way of detecting Breast cancer. However, Mammogram is not effective for women belonging to every age, and hence yields to many false positive and false negative cases. This leads to disastrous results. To overcome this limitation, this experimental study tells, about using one of the Deep Learning algorithm called Single Shot Detector algorithm to detect the cancerous portion in the Mammograms. This experiment was done in the Real Time Data set of mammograms collected from a 1250 bed hospital, after the scientific and Ethical committee approval. With a total of 80 mammography images belonging to either category of being, either Malignant or Benign. The Single Shot Detector algorithm gave an accuracy of 74%, which was better compared with CNN (64%) and VGG (60%) deep learning algorithms.

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Acknowledgments

Authors acknowledge, that this work was carried out in the Big Data Analytics Lab funded by VGST, Govt. of Karnataka, under K-FIST(L2)-545, and the data was collected from Father Muller Medical College, protocol no: (FMMCIEC/CCM/2165/2021).

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Authors and Affiliations

  1. St Aloysius College (Autonomous), Mangalore, India

    S. Ruban & M. M. Jabeer

  2. Father Muller Medical College, Mangalore, India

    Ram Shenoy Besti

Authors
  1. S. Ruban
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  2. M. M. Jabeer
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  3. Ram Shenoy Besti
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Corresponding author

Correspondence to S. Ruban .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, India

    P. K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

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Ruban, S., Jabeer, M.M., Besti, R.S. (2022). Detection of Breast Tumor in Mammograms Using Single Shot Detector Algorithm. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2022. Communications in Computer and Information Science, vol 1614. Springer, Cham. https://doi.org/10.1007/978-3-031-12641-3_30

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  • DOI: https://doi.org/10.1007/978-3-031-12641-3_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12640-6

  • Online ISBN: 978-3-031-12641-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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