Integrated Design of Optimized Weighted Deep Feature Fusion Strategies for Skin Lesion Image Classification

SIMPLE SUMMARY: The reported global incidences of skin cancer led to the development of automated clinical aids for making proper clinical decision models. Correctly classifying the skin lesions during the early stage may increase the chances of being cured before cancer. However, the skin lesion dataset images pose many critical challenges related to available features to develop classification models with cross-domain adaptability and robustness. This paper made an attempt to select important features from skin lesion datasets for proper skin cancer classification by proposing some feature fusion strategies. Three pre-trained models were utilized to select the important features and then an adaptive weighted mechanism of choosing important features was explored to propose model-based and feature-based optimized feature fusion strategies by optimally and adaptively choosing the weights using a meta-heuristic artificial jellyfish algorithm. The empirical evidence shows that choosing the weights of the pre-trained networks adaptively in an optimized way gives a good starting point for initialization to mitigate the chances of exploding or vanishing gradients. ABSTRACT: This study mainly focuses on pre-processing the HAM10000 and BCN20000 skin lesion datasets to select important features that will drive for proper skin cancer classification. In this work, three feature fusion strategies have been proposed by utilizing three pre-trained Convolutional Neural Network (CNN) models, namely VGG16, EfficientNet B0, and ResNet50 to select the important features based on the weights of the features and are coined as Adaptive Weighted Feature Set (AWFS). Then, two other strategies, Model-based Optimized Weighted Feature Set (MOWFS) and Feature-based Optimized Weighted Feature Set (FOWFS), are proposed by optimally and adaptively choosing the weights using a meta-heuristic artificial jellyfish (AJS) algorithm. The MOWFS-AJS is a model-specific approach whereas the FOWFS-AJS is a feature-specific approach for optimizing the weights chosen for obtaining optimal feature sets. The performances of those three proposed feature selection strategies are evaluated using Decision Tree (DT), Naïve Bayesian (NB), Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM) classifiers and the performance are measured through accuracy, precision, sensitivity, and F1-score. Additionally, the area under the receiver operating characteristics curves (AUC-ROC) is plotted and it is observed that FOWFS-AJS shows the best accuracy performance based on the SVM with 94.05% and 94.90%, respectively, for HAM 10000 and BCN 20000 datasets. Finally, the experimental results are also analyzed using a non-parametric Friedman statistical test and the computational times are recorded; the results show that, out of those three proposed feature selection strategies, the FOWFS-AJS performs very well because its quick converging nature is inculcated with the help of AJS.