  Background: Skin cancer is one of the widely seen cancer worldwide and
automatic classification of skin cancer can be benefited dermatology clinics
for an accurate diagnosis. Hence, a machine learning-based automatic skin
cancer detection model must be developed. Material and Method: This research
interests to overcome automatic skin cancer detection problem. A colored skin
cancer image dataset is used. This dataset contains 3297 images with two
classes. An automatic multilevel textural and deep features-based model is
presented. Multilevel fuse feature generation using discrete wavelet transform
(DWT), local phase quantization (LPQ), local binary pattern (LBP), pre-trained
DarkNet19, and DarkNet53 are utilized to generate features of the skin cancer
images, top 1000 features are selected threshold value-based neighborhood
component analysis (NCA). The chosen top 1000 features are classified using the
10-fold cross-validation technique. Results: To obtain results, ten-fold
cross-validation is used and 91.54% classification accuracy results are
obtained by using the recommended pyramidal hybrid feature generator and NCA
selector-based model. Further, various training and testing separation ratios
(90:10, 80:20, 70:30, 60:40, 50:50) are used and the maximum classification
rate is calculated as 95.74% using the 90:10 separation ratio. Conclusions: The
findings and accuracies calculated are denoted that this model can be used in
dermatology and pathology clinics to simplify the skin cancer detection process
and help physicians.
