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Wyszukujesz frazę "Ensemble Learning" wg kryterium: Temat

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    Wyświetlanie 1-2 z 2
    Tytuł:
    An Ensemble of Statistical Metadata and CNN Classification of Class Imbalanced Skin Lesion Data
    Autorzy:
    Nayak, Sachin
    Vincent, Shweta
    Sumathi, K.
    Kumar, Om Prakash
    Pathan, Sameena
    Powiązania:
    https://bibliotekanauki.pl/articles/2055258.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    classification
    Convolutional Neural Networks
    Ensemble Learning
    machine learning
    metadata
    Opis:
    Skin Cancer is one of the most widely present forms of cancer. The correct classification of skin lesions as malignant or benign is a complex process that has to be undertaken by experienced specialists. Another major issue of the class imbalance of data causes a bias in the results of classification. This article presents a novel approach to the usage of metadata of skin lesions images to classify them. The usage of techniques addresses the problem of class imbalance to nullify the imbalances. Further, the use of a convolutional neural network (CNN) is proposed to finetune the skin lesion data classification. Ultimately, it is proven that an ensemble of statistical metadata analysis and CNN usage would result in the highest accuracy of skin color classification instead of using the two techniques separately.
    Źródło:
    International Journal of Electronics and Telecommunications; 2022, 68, 2; 251--257
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ensemble of classifiers based on deep learning for medical image recognition
    Autorzy:
    Gil, Fabian
    Osowski, Stanisław
    Świderski, Bartosz
    Słowińska, Monika
    Powiązania:
    https://bibliotekanauki.pl/articles/2203370.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    breast cancer
    CNN
    deep learning
    ensemble of classifiers
    feature selection
    melanoma
    Opis:
    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.
    Źródło:
    Metrology and Measurement Systems; 2023, 30, 1; 139--156
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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