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Dermatologist-Level Classification of Skin Cancer Using Cascaded Ensembling of Convolutional Neural Network and Handcrafted Features Based Deep Neural Network

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10249740" target="_blank" >RIV/61989100:27240/22:10249740 - isvavai.cz</a>

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/9706441" target="_blank" >https://ieeexplore.ieee.org/document/9706441</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ACCESS.2022.3149824" target="_blank" >10.1109/ACCESS.2022.3149824</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Dermatologist-Level Classification of Skin Cancer Using Cascaded Ensembling of Convolutional Neural Network and Handcrafted Features Based Deep Neural Network

  • Original language description

    Skin cancer is caused due to unusual development of skin cells and deadly type cancer. Early diagnosis is very significant and can avoid some categories of skin cancers, such as melanoma and focal cell carcinoma. The recognition and the classification of skin malignant growth in the beginning time is expensive and challenging. The deep learning architectures such as recurrent networks and convolutional neural networks (ConvNets) are developed in the past, which are proven appropriate for non-handcrafted extraction of complex features. To additional expand the efficiency of the ConvNet models, a cascaded ensembled network that uses an integration of ConvNet and handcrafted features based multi-layer perceptron is proposed in this work. This offered model utilizes the convolutional neural network model to mine non-handcrafted image features and colour moments and texture features as handcrafted features. It is demonstrated that accuracy of ensembled deep learning model is improved to 98.3% from 85.3% of convolutional neural network model.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20201 - Electrical and electronic engineering

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    IEEE Access

  • ISSN

    2169-3536

  • e-ISSN

  • Volume of the periodical

    10

  • Issue of the periodical within the volume

    neuveden

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    13

  • Pages from-to

    17920-17932

  • UT code for WoS article

    000757818400001

  • EID of the result in the Scopus database

    2-s2.0-85124719303