A comparative study on deep feature selection methods for skin lesion classification

Abstract

AbstractMelanoma, a widespread and hazardous form of cancer, has prompted researchers to prioritize dermoscopic image‐based algorithms for classifying skin lesions. Recently, there has been a growing trend in using pre‐trained convolutional neural networks for detecting skin lesions. However, the features extracted from these classifiers may include irrelevant elements, emphasizing the importance of implementing effective feature selection methods. Nevertheless, there has not been a comprehensive study on feature selection methods to enhance the performance of skin lesion detection to date. To identify the most efficient methods, a diverse set of feature selection techniques, including filter, wrapper, embedded, and dimensionality reduction, were applied to images from two well‐known datasets, namely ISIC 2017 and ISIC 2018. According to the results, models trained with features chosen by wrapper techniques outperformed those trained with features chosen by filter and embedded methods. Achieving an accuracy of 0.8333 and an F1‐Score of 0.8291 for the ISIC 2017 dataset, and an F1‐Score of 0.9324 and an accuracy of 0.9350 for the ISIC 2018 dataset, the classification using features obtained via the GWO feature selection technique performed the best.