Comparative study on the classification methods for breast cancer diagnosis

• Autorzy: Qiu Y. , Zhou G. , Zhao Q. , Cichocki A.

Identyfikatory

DOI

10.24425/bpas.2018.125931

• Języki publikacji: EN

Abstrakty

EN

Digital mammography is one of the most widely used approaches for breast cancer diagnosis. Many researchers have demonstrated the superiority of machine learning methods in breast cancer diagnosis using different mammography databases. Since these methods often have different pros and cons, which may confuse doctors and researchers, an elaborate comparison and examination among them is urgently needed for practical breast cancer diagnosis. In this study, we conducted a comprehensive comparative study of the state-of-the-art machine learning methods that are promising in breast cancer diagnosis. For this purpose we analyze the largest mammography diagnosis database: Digital Database for Screening Mammography (DDSM). We considered various approaches for feature extraction including principal component analysis (PCA), nonnegative matrix factorization (NMF), spatial-temporal discriminant analysis (STDA) and those for classification including linear discriminant analysis (LDA), random forests (RaF), k-nearest neighbors (kNN), as well as deep learning methods including convolutional neural networks (CNN) and stacked sparse autoencoder (SSAE). This paper can serve as a guideline and useful clues for doctors who are going to select machine learning methods for their breast cancer computer-aided diagnosis (CAD) systems as well for researchers interested in developing more reliable and efficient methods for breast cancer diagnosis.

Słowa kluczowe

EN

breast cancer; mammography; DDSM; comparative study; deep learning

PL

rak piersi; mammografia; DDSM; badanie porównawcze; uczenie głębokie

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2018
• Tom: Vol. 66, nr 6
• Strony: 841--848
• Opis fizyczny: Bibliogr. 32 poz., rys., tab.

Twórcy

autor

Qiu Y.

• School of Automation, Guangdong University of Technology, Guangzhou, China.

autor

Zhou G.

• School of Automation, Guangdong University of Technology, Guangzhou, China.

autor

Zhao Q.

• School of Automation, Guangdong University of Technology, Guangzhou, China.
• Tensor Learning Unit, RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan.

autor

Cichocki A.

• Skolkovo Institute of Science and Technology (SKOLTECH), 143026 Moscow, Russia
• System Research Institute, Polish Academy of Sciences, Warsaw 00-901, Poland.
• Hangzhou Dianzi University, College of Computer Science, Hangzhou 310018, China

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).