An Innovative Fusion of Deep Learning and the Differential Analyzer Approach (DAA-Deep model) for Enhanced Skin Cancer Detection

Skin cancer, a globally prevalent and potentially life-threatening disease, underscores the critical importance of early detection and treatment. Traditional diagnostic methods heavily rely on visual inspections conducted by dermatologists, which can be prone to human error and constrained by resource limitations. Recent advancements in machine learning and deep learning techniques offer promising prospects for automating the skin cancer detection process. However, this domain still faces persistent challenges, particularly the need for effective feature selection methods to improve model performance, interpretability, and computational efficiency. The primary goal of this research is to significantly elevate the precision and efficiency of skin cancer detection. To achieve this, a novel fusion of the Differential Analyzer Approach (DAA) with deep learning models has been proposed. This work leverages the ISIC 2018 dataset of skin cancer images, intricate convolutional neural network (CNN) architectures, and a seamlessly integrated DAA mechanism. The proposed DAA-Deep learning model outperforms conventional deep learning models, showcasing higher accuracy and improved diagnostic capabilities. The results achieved by the DAA-Deep model surpasses the performance of numerous existing models, exhibiting a remarkable accuracy rate of 96% along with an impressive AUC (Area under the ROC Curve) value of 0.99.