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

doi:10.1109/ISCAS46773.2023.10181859

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 proceeding › Conference contribution › peer-review

4 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 language	English (US)
Title of host publication	ISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665451093
DOIs	https://doi.org/10.1109/ISCAS46773.2023.10181859
State	Published - 2023
Event	56th IEEE International Symposium on Circuits and Systems, ISCAS 2023 - Monterey, United States Duration: May 21 2023 → May 25 2023

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	2023-May
ISSN (Print)	0271-4310

Conference

Conference	56th IEEE International Symposium on Circuits and Systems, ISCAS 2023
Country/Territory	United States
City	Monterey
Period	05/21/23 → 05/25/23

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ISCAS46773.2023.10181859

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Khalid, N., Hashir, M., Mahmood, N., Asad, M., Rehman, M. A., Mehmood, M. Q., Zubair, M., & Massoud, Y. (2023). Efficient Deep Learning Approaches for Automated Tumor Detection, Classification, and Localization in Experimental Microwave Breast Imaging Data. In ISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2023-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS46773.2023.10181859

Khalid, Nazish ; Hashir, Muhammad ; Mahmood, Nasir et al. / Efficient Deep Learning Approaches for Automated Tumor Detection, Classification, and Localization in Experimental Microwave Breast Imaging Data. ISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings - IEEE International Symposium on Circuits and Systems).

@inproceedings{a83c0098d87d441b96ec0f451aa87c13,

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

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.",

keywords = "Convolutional Neural Networks (CNN), Deep learning (DL), Deep Neural Networks (DNN), localization, Machine learning (ML), Microwave Imaging (MWI), Residual Neural Networks (ResNet)",

author = "Nazish Khalid and Muhammad Hashir and Nasir Mahmood and Muhammad Asad and Rehman, {Muhammad A.} and Mehmood, {Muhammad Q.} and Muhammad Zubair and Yehia Massoud",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 56th IEEE International Symposium on Circuits and Systems, ISCAS 2023 ; Conference date: 21-05-2023 Through 25-05-2023",

year = "2023",

doi = "10.1109/ISCAS46773.2023.10181859",

language = "English (US)",

series = "Proceedings - IEEE International Symposium on Circuits and Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "ISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings",

address = "United States",

}

Khalid, N, Hashir, M, Mahmood, N, Asad, M, Rehman, MA, Mehmood, MQ, Zubair, M & Massoud, Y 2023, Efficient Deep Learning Approaches for Automated Tumor Detection, Classification, and Localization in Experimental Microwave Breast Imaging Data. in ISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings. Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2023-May, Institute of Electrical and Electronics Engineers Inc., 56th IEEE International Symposium on Circuits and Systems, ISCAS 2023, Monterey, United States, 05/21/23. https://doi.org/10.1109/ISCAS46773.2023.10181859

Efficient Deep Learning Approaches for Automated Tumor Detection, Classification, and Localization in Experimental Microwave Breast Imaging Data. / Khalid, Nazish; Hashir, Muhammad; Mahmood, Nasir et al.
ISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2023-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Efficient Deep Learning Approaches for Automated Tumor Detection, Classification, and Localization in Experimental Microwave Breast Imaging Data

AU - Khalid, Nazish

AU - Hashir, Muhammad

AU - Mahmood, Nasir

AU - Asad, Muhammad

AU - Rehman, Muhammad A.

AU - Mehmood, Muhammad Q.

AU - Zubair, Muhammad

AU - Massoud, Yehia

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Convolutional Neural Networks (CNN)

KW - Deep learning (DL)

KW - Deep Neural Networks (DNN)

KW - localization

KW - Machine learning (ML)

KW - Microwave Imaging (MWI)

KW - Residual Neural Networks (ResNet)

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U2 - 10.1109/ISCAS46773.2023.10181859

DO - 10.1109/ISCAS46773.2023.10181859

M3 - Conference contribution

AN - SCOPUS:85167666699

T3 - Proceedings - IEEE International Symposium on Circuits and Systems

BT - ISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 56th IEEE International Symposium on Circuits and Systems, ISCAS 2023

Y2 - 21 May 2023 through 25 May 2023

ER -

Khalid N, Hashir M, Mahmood N, Asad M, Rehman MA, Mehmood MQ et al. Efficient Deep Learning Approaches for Automated Tumor Detection, Classification, and Localization in Experimental Microwave Breast Imaging Data. In ISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings - IEEE International Symposium on Circuits and Systems). doi: 10.1109/ISCAS46773.2023.10181859

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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