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

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.

Passive Microwave Radiometry (MWR) and Mirna for Breast Cancer Diagnostics

“…The results are an indication of the potential of MWR utilizing a neural network-based diagnostic tool for cancer detection. [10] At the same time, with further study of this technique, data were obtained showing the limitations of the MWR in some pathologies of the mammary gland. First, with the slow growth of a malignant neoplasm, heat dissipation is observed, which is masked by the heat generated by the surrounding tissues.…”