Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Dynamic Weight Agnostic Neural Networks and Medical Microwave Radiometry (MWR) for Breast Cancer Diagnostics

Jolen Li
ORCID logo ,
Christopher Galazis
,
Illarion Popov
,
Lev Ovchinnikov
ORCID logo ,
Sergey Vesnin
ORCID logo ,
Alexander Losev
,
Igor Goryanin
*
Version 1 : Received: 22 July 2022 / Approved: 25 July 2022 / Online: 25 July 2022 (10:04:03 CEST)

A peer-reviewed article of this Preprint also exists.

Li, J.; Galazis, C.; Popov, L.; Ovchinnikov, L.; Kharybina, T.; Vesnin, S.; Losev, A.; Goryanin, I. Dynamic Weight Agnostic Neural Networks and Medical Microwave Radiometry (MWR) for Breast Cancer Diagnostics. Diagnostics 2022, 12, 2037. Li, J.; Galazis, C.; Popov, L.; Ovchinnikov, L.; Kharybina, T.; Vesnin, S.; Losev, A.; Goryanin, I. Dynamic Weight Agnostic Neural Networks and Medical Microwave Radiometry (MWR) for Breast Cancer Diagnostics. Diagnostics 2022, 12, 2037.

Abstract

Abstract Background and Objective: Medical Microwave Radiometry (MWR) is used to capture the thermal properties of internal tissues and has usages in breast cancer detection. Our goal in this paper is to improve classification performance and investigate automated neural architecture search methods. Methods: We investigate optimizing the weights of a weight agnostic neural network using bi-population covariance matrix adaptation evolution strategy (BIPOP-CMA-ES) once the topology is found. We compare it against a weight agnostic and cascade correlation neural network. Results: The experiments are conducted on a breast cancer dataset of 4912 patients. Our proposed weight agnostic BIPOP-CMA-ES model achieved the best performance. It obtained an F1-score of 0.9225, accuracy of 0.9219, precision of 0.9228, recall of 0.9217 and topology of 153 connections. Conclusions: The results are an indication of the potential of MWR utilizing a neural network-based diagnostic tool for cancer detection. By separating the tasks of topology search and weight training, we are able to improve the overall performance.

Keywords

breast cancer; passive microwave radiometry (MWR); cascaded correlation neural network (CCNN); weight agnostic neural network (WANN); CMA-ES algorithm.

Subject

Medicine and Pharmacology, Oncology and Oncogenics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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