Usability of CNN and Attention Mechanisms for Classifying Melanoma Image
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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