Hybrid of Deep Feature Extraction and Machine Learning Ensembles for Imbalanced Skin Cancer Datasets

Abstract

Skin cancer remains one of the most common and deadly forms of cancer, necessitating accurate and early diagnosis to improve patient outcomes. In order to improve classification performance on unbalanced datasets, this study proposes a distinctive approach for classifying skin cancer that utilises both machine learning (ML) and deep learning (DL) methods. We extract features from three different DL models (DenseNet201, Xception, Mobilenet) and concatenate them to create an extensive feature set. Afterwards, several ML algorithms are given these features to be classified. We utilise ensemble techniques to aggregate the predictions from several classifiers, significantly improving the classification's resilience and accuracy. To address the problem of data imbalance, we employ class weight updates and data augmentation strategies to ensure that the model is thoroughly trained across all classes. Our method shows significant improvements over recent existing approaches in terms of classification accuracy and generalisation. The proposed model successfully received 98.7%, 94.4% accuracy, 99%, 95%, precision, 99%, 96% recall, 99%, and 96% f1-score for the HAM10000 and ISIC datasets, respectively. This study offers dermatologists and other medical practitioners' valuable insights into the classification of skin cancer.