Deep Learning-Based Optimization of Dolphin Echolocation for Skin Lesion Image Classification

The healthcare industry has benefited greatly from the recent developments in Machine Learning (ML), especially deep learning (DL). This motivates the study of how to best employ AI, and deep learning (DL) algorithms in particular, to identify skin lesions in dermoscopic pictures. To do this, the model employs an Inventive Dolphin Echolocation Optimization Deep Learning driven Skin Lesion Detection and Classification (IDEODL-SLDC) technique to analyse these images and improve upon a previously developed Dolphin Echolocation Optimizer (IDEO). In addition, the SqueezeNet method can be used to obtain feature vectors. The dermoscopy images are also classified using IDEO and a deep wavelet neural network (DWNN) approach. The IDEODL-SLDC method is improved on the reference dataset after undergoing extensive testing. The experimental results demonstrated that the IDEODL-SLDC model exhibited superior performance compared to alternative approaches across different dimensions of model classifier classes.