Outperforming Clinical Practices in Breast Cancer Detection: A Superior Dense Neural Network in Classification and False Negative Reduction

TY - CONF

T1 - Outperforming Clinical Practices in Breast Cancer Detection

T2 - A Superior Dense Neural Network in Classification and False Negative Reduction

AU - Bujok, Patrick

AU - Jensen, Maria

AU - Larsen, Steffen M.

AU - Alphinas, Robert

PY - 2021/12

Y1 - 2021/12

N2 - Machine Learning applications provide a promising method to support clinical practitioners in Breast Cancer (BC) detection. Currently, Fine Needle Aspiration (FNA) is a commonly applied diagnostic method for BC tumors, which,however, is associated with ominous false negative misclassifications. For this purpose, the present study explores Artificial Neural Networks (ANNs) with the aim of outperforming clinical practices via FNA in classifying benign or malignant BC cases with regard to an improved accuracy and reduced False NegativeRate (FNR) using the Breast Cancer Wisconsin (Diagnostic) Dataset (WDBC). The findings reveal that a dense ANN with a single hidden layer including 15 neurons can reach a testing accuracy of 98.60% and a FNR of 0% on a scaled dataset. Incombination with several introduced improvement measures, a high degree of generalizability is associated with the model under the consideration of the relatively small dataset. As a result, this model outperforms not only clinical practitioners but also 72 classifiers from the recent literature.

AB - Machine Learning applications provide a promising method to support clinical practitioners in Breast Cancer (BC) detection. Currently, Fine Needle Aspiration (FNA) is a commonly applied diagnostic method for BC tumors, which,however, is associated with ominous false negative misclassifications. For this purpose, the present study explores Artificial Neural Networks (ANNs) with the aim of outperforming clinical practices via FNA in classifying benign or malignant BC cases with regard to an improved accuracy and reduced False NegativeRate (FNR) using the Breast Cancer Wisconsin (Diagnostic) Dataset (WDBC). The findings reveal that a dense ANN with a single hidden layer including 15 neurons can reach a testing accuracy of 98.60% and a FNR of 0% on a scaled dataset. Incombination with several introduced improvement measures, a high degree of generalizability is associated with the model under the consideration of the relatively small dataset. As a result, this model outperforms not only clinical practitioners but also 72 classifiers from the recent literature.

KW - Machine Learning

KW - Breast Cancer

KW - Artificial Neural Network

KW - Classification

KW - WDBC

M3 - Paper

ER -