Abstract
ABSTRACT In this research, an advanced deep learning model is proposed to segment and classify skin diseases. The skin images required for classification purposes are collected from public databases. Skin lesion segmentation is initially performed over collected images using Multi-head Attention-based MobileUNet, which helps locate lesions in dermoscopic images. The segmented images are fed to the feature extraction stage to get more representative features from the images to improve the classification performance. Features like texture, shape, and colour are extracted in this stage. The extracted features are given to the Adaptive Deep Capsule Network to classify the images. The segmented images are directly given to the Adaptive MobileNet to provide classification results. The final decision is made by averaging the classified outcome using the Adaptive Hybrid Network. The performance of classification is further enhanced by tuning the parameters from DCNet and MNet using the Enhanced Secretary Bird Optimisation. The performance validation finally shows the accurate performance in the skin disease classification model.
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: Australian Journal of Electrical and Electronics Engineering
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.
