Ensemble of classifiers based on deep learning for medical image recognition

• Autorzy: Gil Fabian , Osowski Stanisław , Świderski Bartosz , Słowińska Monika

Identyfikatory

DOI

10.24425/mms.2023.144400

• Języki publikacji: EN

Abstrakty

EN

The paper presents special forms of an ensemble of classifiers for analysis of medical images based on application of deep learning. The study analyzes different structures of convolutional neural networks applied in the recognition of two types of medical images: dermoscopic images for melanoma and mammograms for breast cancer. Two approaches to ensemble creation are proposed. In the first approach, the images are processed by a convolutional neural network and the flattened vector of image descriptors is subjected to feature selection by applying different selection methods. As a result, different sets of a limited number of diagnostic features are generated. In the next stage, these sets of features represent input attributes for the classical classifiers: support vector machine, a random forest of decision trees, and softmax. By combining different selection methods with these classifiers an ensemble classification system is created and integrated by majority voting. In the second approach, different structures of convolutional neural networks are directly applied as the members of the ensemble. The efficiency of the proposed classification systems is investigated and compared to medical data representing dermoscopic images of melanoma and breast cancer mammogram images. Thanks to fusion of the results of many classifiers forming an ensemble, accuracy and all other quality measures have been significantly increased for both types of medical images.

Słowa kluczowe

EN

breast cancer; CNN; deep learning; ensemble of classifiers; feature selection; melanoma

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2023
• Tom: Vol. 30, nr 1
• Strony: 139--156
• Opis fizyczny: Bibliogr. 32 poz., rys., tab., wykr.

Twórcy

autor

Gil Fabian

• Military University of Technology, Faculty of Electronics, Institute of Electronic Systems, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland

autor

Osowski Stanisław

• Military University of Technology, Faculty of Electronics, Institute of Electronic Systems, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
• Warsaw University of Technology, Faculty of Electrical Engineering, pl. Politechniki 1, 00-661 Warsaw, Poland

autor

Świderski Bartosz

• University of Life Sciences, ul. Nowoursynowska 166, 02-787 Warsaw

autor

Słowińska Monika

• Central Clinical Hospital Ministry of Defense, Military Institute of Medicine - National Research Institute, ul. Szaserów 128, 04-141 Warsaw

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).