ArticleDiagnostics (Basel, Switzerland)2022
Dynamic Weight Agnostic Neural Networks and Medical Microwave Radiometry (MWR) for Breast Cancer Diagnostics.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- A Resonator-Based Flexible Antenna for Non-Invasive Deep Brain Temperature Sensing with Microwave Radiometry.Sensors (Basel, Switzerland) · 2026Article
- Machine Learning in Microwave Medical Imaging and Lesion Detection.Diagnostics (Basel, Switzerland) · 2025Review
- Breast Cancer Detection via Multi-Tiered Self-Contrastive Learning in Microwave Radiometric Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- The Challenge of Deep Learning for the Prevention and Automatic Diagnosis of Breast Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Emerging paradigms in microwave imaging technology for biomedical applications: unleashing the power of artificial intelligence.Npj imaging · 2024Review
- Passive Microwave Radiometry and microRNA Detection for Breast Cancer Diagnostics.Diagnostics (Basel, Switzerland) · 2022Article
- Diagnostics of Ovarian Tumors in Postmenopausal Patients.Diagnostics (Basel, Switzerland) · 2022Review
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Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveMedical 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.
methodsWe 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.
resultsThe 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.
conclusionsThe 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.
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