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 investigated extending the weight agnostic neural network by optimizing the weights using the bi-population covariance matrix adaptation evolution strategy (BIPOP-CMA-ES) once the topology was found. We evaluated and compared the model based on the F1 score, accuracy, precision, recall, and the number of connections. Results: The experiments were conducted on a dataset of 4912 patients, classified as low or high risk for breast cancer. The weight agnostic BIPOP-CMA-ES model achieved the best average performance. It obtained an F1-score of 0.933, accuracy of 0.932, precision of 0.929, recall of 0.942, and 163 connections. Conclusions: The results of the model are an indication of the promising potential of MWR utilizing a neural network-based diagnostic tool for cancer detection. By separating the tasks of topology search and weight training, we can improve the overall performance.

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.