Abstract: This research paper introduces a novel framework for skin disease classification, combining Wavelet Attention VGG19 and XGBoost algorithms. Wavelet Attention VGG19 leverages the power of deep learning and wavelet attention mechanisms to extract discriminative features from skin lesion images, while XGBoost, a gradient boosting technique, complements the feature extraction capabilities with its ability to handle complex data relationships. The integration of these methodologies aims to improve accuracy and resilience in binary skin disease classification. The two goals of this study are to first improve feature learning and representation from skin lesion images by introducing wavelet attention into the VGG19 architecture, and second to improve classification performance by utilising XGBoost's ensemble and generalisation capabilities.
Keywords: Wavelet Attention, Xgboost, deep learning, convolutional neural network, skin disease classification, VGG19.
Title: Wavelet Attention VGG19 and XGBOOST for Classification of Skin Disease
Author: Samaila Audu, Ali Ahmad Aminu
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Vol. 11, Issue 4, October 2023 - December 2023
Page No: 5-13
Research Publish Journals
Website: www.researchpublish.com
Published Date: 07-October-2023
