An Analysis of Feature Extraction in Image Classification: A Comprehensive Study

There have been many real-life applications utilizing deep learning, especially in the area of image classification. A common finding is that some domain data are highly skewed, which means that most of the information belongs to a small number of majority classes, and there is little or no information in the minority classes. Due to which in case of imbalanced data distribution, the majority of machine and deep learning algorithms are not effective or may fail when it is highly imbalanced. In this study, a comprehensive analysis of imbalanced dataset is conducted by considering deep learning-based well-known models. In particular, the best feature extractor model is identified and the current trend of latest feature extraction model is examined. Moreover, a bibliometric analysis is carried out from 1991 to 2022 in order to identify the global scientific research on the image classification of imbalanced mushroom dataset. In summary, our findings may offer researchers a quick benchmarking reference and alternative approach to assessing trends in imbalanced data distributions in image classification research.