Melanoma Skin Cancer Classification Using EfficientNetB7 for Deep Feature Extraction and Ensemble Learning Approach

Cancer is one of the deadliest diseases in the world. cancer is caused by the presence of cancer cells due to abnormal conditions during the cell turnover process. One of the dangerous types of cancer is melanoma skin cancer, this cancer attacks the outer skin of humans because skin cells are prone to damage. However, diagnosis for this disease is mostly done manually while there are previous studies that use deep learning approaches with the accuracy that can be improved. The purpose of this study is to find an effective and efficient method for melanoma cancer recognition so that it can be treated more quickly. We propose several methods that we have compared to be able to classify melanoma skin cancer with EfficientNetB7 Feature Extractor and Ensemble Learning. The results of this research model get the highest accuracy of 91.2%. When EfficientNetB7 together with ensemble learning. This research model has better and efficient results when compared to previous research.