Abstract
The successful treatment of skin cancer and better patient outcomes depend on an early and precise diagnosis.This work offers a multiresolution assessment of the contourlet transform for the diagnosis of skin cancer, utilizing its capacity to catch fine features in images at many scales and orientations. The contourlet transform is applied to dermoscopic images to enhance feature extraction, providing a more comprehensive representation of skin lesions compared to traditional methods. The proposed method involves preprocessing dermoscopic images to improve clarity and reduce noise, followed by the application of the contourlet transform to decompose the images into various frequency bands. These decomposed images are then analyzed to extract relevant textural and structural features, which are subsequently used to train a machine learning classifier. A collection of annotated skin lesion photos is used for performance evaluation, and the outcomes are compared with state-of-the-art methods currently in use. The efficacy of the suggested method is evaluated using metrics including sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve. The findings show that the contourlet transform-based approach performs better than traditional methods in capturing important characteristics of skin lesions, improving the ability to distinguish benign from malignant lesions and improving diagnostic accuracy. The contourlet transform is a formidable tool for the multiresolution analysis of skin cancer images, according to the study's conclusion, and it has a lot of promise for enhancing dermatology computer-aided diagnosis systems.