Dermo-Optimizer: Skin Lesion Classification Using Information-Theoretic Deep Feature Fusion and Entropy-Controlled Binary Bat Optimization

Abstract

Increases in the prevalence of melanoma, the most lethal form of skin cancer, have been observed over the last few decades. However, the likelihood of a longer life span for individuals is considerably improved with early detection of this malignant illness. Even though the field of computer vision has attained a certain level of success, there is still a degree of ambiguity that represents an unresolved research challenge. In the initial phase of this study, the primary objective is to improve the information derived from input features by combining multiple deep models with the proposed Information-theoretic feature fusion method. Subsequently, in the second phase, the study aims to decrease the redundant and noisy information through down-sampling using the proposed entropy-controlled binary bat selection algorithm. The proposed methodology effectively maintains the integrity of the original feature space, resulting in the creation of highly distinctive feature information. In order to obtain the desired set of features, three contemporary deep models are employed via transfer learning: Inception-Resnet V2, DenseNet-201, and Nasnet Mobile. By combining feature fusion and selection techniques, we may effectively fuse a significant amount of information into the feature vector and subsequently remove any redundant feature information. The effectiveness of the proposed methodology is supported by an evaluation conducted on three well-known dermoscopic datasets, specifically ${\mathrm{PH}}^2$, ISIC-2016, and ISIC-2017. In order to validate the proposed approach, several performance indicators are taken into account, such as accuracy, sensitivity, specificity, false negative rate (FNR), false positive rate (FPR), and F1-score. The accuracies obtained for all datasets utilizing the proposed methodology are 99.05%, 96.26%, and 95.71%, respectively.