AI-Driven Skin Cancer Detection: A Deep Learning Perspective

Abstract

The rising incidence of skin cancer necessitates the development of accurate and efficient diagnostic methods. This study introduces a novel approach for detecting skin cancer using an encoding-decoding technique with convolutional neural networks (CNNs) to enhance feature extraction from skin lesion images. Advanced CNN architectures, including DenseNet201, VGG16, and Xception, are utilized to classify skin lesions into seven categories. A thorough evaluation on a large dataset confirms the effectiveness of the proposed method in accurately identifying various types of skin cancer. Furthermore, a comparative study of multiple CNN models provides key insights into their relative strengths and limitations for diagnostic purposes. This research contributes to improving computer-aided skin cancer detection, paving the way for more reliable and accessible screening solutions.