Skin cancer classification using explainable artificial intelligence on pre-extracted image features

Skin cancer is the most common type of cancer worldwide, affecting a large population recently. To date, various machine learning techniques exploiting skin images have been applied directly to skin cancer classification, showing promising results in improving diagnostic accuracy. This study aims to develop a machine learning-based model capable of accurately classifying skin cancer by utilizing extracted features from preprocessed images in the publicly available PH² dataset. Preprocessed features are known to provide more significant in- formation than raw image data, as they capture specific characteristics of the images that are relevant to the classification task. The proposed model of this study can identify the most pertinent information in the images more accurately, thereby improving the performance and interpretability of the machine learning classification. Our simulation results illustrate that employing XG-boost yields an accuracy of 94% and an area under the curve value of 0.9947, further indicat- ing that the proposed technique effectively distinguishes between non-melanoma and melanoma skin cancer. Explainable artificial intelligence provides some ex- planations by leveraging model-agnostic methods such as partial dependence plot, permutation importance, and SHAP. Moreover, the explainable artificial intelligence results show that asymmetry and pigment network features are the most important feature in the classification of skin cancer. These specific charac- teristics emerge as the most influential factors in distinguishing between different types of skin cancer.