Modelling of Whale Optimization Algorithm with Deep Learning for Skin Cancer Diagnosis on Dermoscopic Images

Abstract

Skin cancer diagnoses through the analysis of dermoscopy images have undergone a revolutionary change with the incorporation of advanced Machine Learning (ML) approaches. By using contemporary models such as Convolutional Neural Network (CNN) and Deep Learning (DL) structures, this technique allows automated and accurate detection of malignant and benign skin tumours. This model learns intricate patterns and features directly from the image, which allows them to distinguish between different types of skin tumours based on colour, texture, and structural characteristics. This manuscript presents the Whale Optimization Algorithm with Deep Learning for Skin Cancer Diagnoses on Dermoscopy Images (WOADL-SCDDI) technique. The objective of the WOADL-SCDDI method is to analyze the Dermoscopic images for classifying and recognizing skin tumours. In the proposed WOADL-SCDDI technique, median filtering-based preprocessing and SegNet-based segmentation are involved. In addition, the WOADL-SCDDI technique offers a ResNet50 model for feature extraction with WOA-based hyperparameter tuning. Finally, Quasi Recurrent Neural Network (QRNN) model can be applied for skin tumour recognition and classification process. A comprehensive set of simulations have been conducted to highlight the better efficiency of the WOADL-SCDDI technique. The accomplished outcomes portrayed the improvements of the WOADL-SCDDI method compared to existing DL models.