Usability of CNN and Attention Mechanisms for Classifying Melanoma Image
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2021-07-02
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en
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Abstract
Malignant melanoma accounts for about 2% of all malignancies in the West-
ern countries, particularly in the United States, and is a disease that kills
more than 9,000 people each year. In general, skin lesions are di cult to
detect accurately through visual criteria, but if they are detected well at
an early stage, unnecessary time and cost for additional diagnosis can be
reduced. This study proposes a solution using a deep learning-based CNN
to solve the problem of skin cancer classi cation. Preprocessing to solve the
class imbalance problem is performed and transfer learning architecture to
select a backbone architecture model and train it successfully. Furthermore,
we apply one of the new deep learning techniques, so-called 'Attention', to
the existing model to nd out whether the model architecture replaced by
the attention layer has better performance. As a result, it is expected that
several proposed arti cial intelligence algorithms will be utilized to build
better computer-aided diagnostic algorithms, which will help early detec-
tion of malignant melanoma.
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Faculteit der Sociale Wetenschappen