Response to Comments on "Detection and Localization of Breast Cancer Using UWB Microwave Technology and CNN-LSTM Framework"

• Published in: IEEE transactions on microwave theory and techniques Vol. 71; no. 10; p. 1
• Main Authors: Lu, Min, Xiao, Xia, Pang, Yanwei, Liu, Guancong, Lu, Hong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Breast; Breast backscatter signals; convolutional neural network (CNN); Convolutional neural networks; Datasets; Dielectric properties; Localization; Location awareness; long short-term memory (LSTM); Microwave circuits; Microwave imaging; Microwave integrated circuits; tumor detection and localization; Tumors
• ISSN: 0018-9480; 1557-9670
• DOI: 10.1109/TMTT.2023.3264555

This article is a response to comments on the above article (Lu et al., 2022) by Reimer and Pistorius (2022) and Reimer et al. (2020). The discussion section of Lu et al. (2022) had cited and compared a relevant article (Al Khatib, Nov. 2021) regarding tumor detection and localization using the sizable experimental dataset (Reimer et al., 2020 and Breast backscatter signals, convolutional neural network (CNN) classifier, see "ref 20" in Table V of Lu et al. (2022). In addition, the diversity of the dataset broadens the data distribution, which is conducive to enhancing the network generalization performance. Our ongoing work will further enrich the dataset diversity by introducing the factor of the adipose shell as discussed by Reimer and Pistorius (2022), as well as other influencing factors, such as fibroglandular shape, and differences in dielectric properties. We appreciate Tyson Reimer and Dr. Stephen Pistorius for the careful comments on Lu et al. (2022).