Neural Networks in Diagnosis of Breast Cancer

Losev, Alexander; Medevedev, Denis A.; Svetlov, Andrey

doi:10.1007/978-3-030-59126-7_25

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Figure 3. Capture positions for each mammary gland (0-8) and at the axillary point (9). Additional T1 and T2 reference points under the chest.…”

Section: Datamentioning

confidence: 99%

“…This is done by measuring the naturally omitted thermal radiation from the tissues. Due to the device's accuracy, non-invasive, non-ionizing and cost-effective characteristics, there are already multiple clinical applications using the temperature readings to identify various conditions [1][2][3][4][5][6][7][8][9][10][11], such as breast cancer that is investigated here. This is feasible because the growth rate of tumors is correlated with the tissues' temperature [12,13].…”

Section: Introductionmentioning

confidence: 99%

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

Li¹,

Galazis²,

Popov³

et al. 2022

Preprint

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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.

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“…Figure 3. Capture positions for each mammary gland (0-8) and at the axillary point (9). Additional T1 and T2 reference points under the chest.…”

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Li¹,

Galazis²,

Popov³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Additionally, it is a noninvasive, nonionizing, and cost-effective approach. Due to the device’s accuracy, there are already multiple clinical applications using the temperature readings and patterns to identify various conditions [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. In this paper, we focus on using MWR to detect breast cancer.…”

Section: Introductionmentioning

confidence: 99%

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

Galazis

Popov

et al. 2022

Diagnostics

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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.

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Cluster Analysis of the Results of Numerical Simulation of Thermometric Data

Polyakov

2021

2021 International Conference on Information Technology and Nanotechnology (ITNT)

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Neural Networks in Diagnosis of Breast Cancer

Cited by 5 publications

References 7 publications

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

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

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

Cluster Analysis of the Results of Numerical Simulation of Thermometric Data

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