Abstract
AbstractThe two complex skin cancer type is melanoma and non‐melanoma. However, the classification of skin cancer is not performed accurately due to the requirement of a huge time and the presence of noisy images. The severity level is not detected and this leads to generate serious issues. To overcome these entire issues GSO‐optimized kernel random forest (RF)‐based transfer learning (TL) is proposed to perform the classification of 12 different skin cancer types by using ImageNet and CNN architectures. The kernel decision tree is taken into account by the RF classifier throughout the classification process, and the RF parameters are modified using the GSO algorithm. The proposed algorithm not only effectively enhances the detection results based on previous adaptive thresholding segmentation techniques but also extends the skin cancer detection process. Compared to existing MMCL, SVM, Mask‐RCNN, and CNN methods the proposed technique attained better performance in classification of skin cancer.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Imaging Systems and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
