ADTBO: Aquila driving training-based optimization with deep learning for skin cancer detection

Abstract

ABSTRACT Generally, melanoma skin disease is also a type of cancer, which is complex to determine. In case, various skin cancer diseases are identified at an early stage, then the death rate is to be reduced. Medical imaging technology acts as an important role in perceiving these types of skin lesions perfectly. This research introduced the automatic skin cancer detection technique, namely Aquila Driving Training-Based optimization (ADTBO) with Squeeze Net. The ADTBO is derived by the combination of Aquila Optimizer (AO) and Driving Training-Based optimization (DTBO). The skin cancer segmentation is completed by the Minkowski-based dual network, where the dual network encompasses the PsiNet and Deep Joint segmentation. The data augmentation and feature extraction are completed to enhance the effectiveness of detection. The ADTBO-SqueezeNet achieved the higher performance based on the accuracy of 0.976, specificity of 0.984 and sensitivity of 0.987. The segmentation accuracy achieved by the Minkowski-based Dual Network is 0.948.