Abstract

Skin cancer is most top three critical kinds of cancer due to damaged DNA, which is cause death. This damaged DNA begins cells for growing uncontrollably and currently it can be obtaining improved quickly. It is several researches on the computerized examination of malignancy from the skin cancer image. But, study of these images are very difficult taking several troublesome issues such as light reflections on the skin surface, differences from the color illumination, sizes of lesions, and distinct shapes. Thus, the outcome, evidential automatic detection of skin cancer are appreciated for developing the accuracy and efficiency of pathologists at the beginning phases. This manuscript develops a Stacked Ensemble Machine Learning based Skin Cancer Detection and Classification (SEML-SKCDC) approach. The presented SEML-SKCDC technique majorly aims to offer ensemble of three ML models for skin cancer classification. In the presented SEML-SKCDC technique, median filtering and contrast enhancement is performed at the pre-processing stage. To generate feature vectors, the honey badger algorithm (HBA) with EfficientNet method has been exploited in this work. At last, an ensemble of k-nearest neighbor (KNN), random forest (RF), and feed forward neural network (FFNN) approaches are applied for skin cancer classification. The simulation evaluation of the SEML-SKCDC system on skin cancer database depicts the developments of the SEML-SKCDC algorithm with recent methods.