Analysis Of Skin Lesion With Ensemble Learning Classifiers And Image Denoising

Abstract

The screening of skin cancer at a tender age and at the correct time is the only way of ensuring that more patients survive cancer as skin cancer remains one of the most common forms of malignancies in the world today. Computerized skin lesion analysis has been of much interest as it can help dermatologists in the early diagnosis of lesions and minimize the errors of subjectivity in their interpretation. This paper is a new ensemble-based skin lesion classification model that combines both deep learning and machine learning to ensure effective and dependable diagnostic accuracy. A Convolutional Neural Network (CNN) is used in the proposed method to extract features of dermoscopic images according to their complex space and texture features of skin lesions automatically. Deep feature is then classified with the help of Random Forest and Extreme Gradient Boosting (XGBoost) classifiers. To even increase the predictive accuracy and generalization power, a hybrid ensemble approach based on stacking and weighted averaging are proposed, which can conveniently make use of the complementary strengths of models. The framework is tested on the publicly available HAM10000 dataset that consists of various categories of pigmented skin lesions of different visual appearances. A long series of experimental results show that the suggested ensemble model has a dramatically positive advantage in performance regarding accuracy, sensitivity, specificity as well as general reliability over the individual classifiers and traditional single-model practices. The findings help to emphasize the success of the integration of deep feature learning and ensemble decision-making. The acquired system is a valid and automatic instrument in detecting skin cancer in order to aid the process of early diagnosis, clinical decision-making, and these factors may be used to reduce the development of tumors and increase the survival rates of patients.