Efficient Deep Learning Approaches for Automated Tumor Detection, Classification, and Localization in Experimental Microwave Breast Imaging Data

Nazish Khalid, Muhammad Hashir, Nasir Mahmood, Muhammad Asad, Muhammad A. Rehman, Muhammad Q. Mehmood, Muhammad Zubair*, Yehia Massoud*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Breast Microwave Imaging (BMI) has emerged as a competitive and potentially disruptive alternative to conventional breast cancer screening techniques owing to its desirable features and improved detection rate. In this paper, we apply various artificial intelligence, and deep learning approaches for automatic breast tumor detection, classification and localization in an open-source experimental BreastCare dataset obtained using our pre-clinical, portable and cost-effective BMI system. We compare the effectiveness of various cutting-edge machine-learning detection algorithms to assess the usefulness of the obtained data-set. Also, we present a deep learning framework that outperforms state-of-the-art microwave imaging methods and ML algorithms for tumor detection, localization, and characterization. The proposed framework gives promising results using our BMI system's measured reflection coefficients (S11). This work shows the potential advantages of applying cutting-edge deep learning algorithms in practical BMI systems.

Original languageEnglish (US)
Title of host publicationISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665451093
DOIs
StatePublished - 2023
Event56th IEEE International Symposium on Circuits and Systems, ISCAS 2023 - Monterey, United States
Duration: May 21 2023May 25 2023

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2023-May
ISSN (Print)0271-4310

Conference

Conference56th IEEE International Symposium on Circuits and Systems, ISCAS 2023
Country/TerritoryUnited States
CityMonterey
Period05/21/2305/25/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Convolutional Neural Networks (CNN)
  • Deep learning (DL)
  • Deep Neural Networks (DNN)
  • localization
  • Machine learning (ML)
  • Microwave Imaging (MWI)
  • Residual Neural Networks (ResNet)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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