CJT-DEO: Condorcet’s Jury Theorem and Differential Evolution Optimization based ensemble of deep neural networks for pulmonary and Colorectal cancer classification

Abstract

Cancer is one of the most dangerous diseases globally, causing adverse effects on human life, with early detection and treatment planning being crucial for patients. Amongst different malignancies, Lung and Colorectal cancer cause the first and second most cancer deaths in the world, respectively. In this study, the authors aim to analyze LC25000 histopathological image dataset for lung and colon cancer detection. The fundamental goal of the proposed research is to leverage the ensemble learning approach to improve the classification performance of deep learning models. Many previous studies have proposed several ensemble methods and weighting schemes. However, none of them optimized the assigned weights using a meta-heuristic-based approach as per our best knowledge. The authors have applied Differential Evolution optimization to optimize and find the optimal assigned weights to the classifiers while training the ensemble model. In addition, a novel approach to ensemble base learners with majority voting based on Condorcet’s Jury Theorem has also been proposed. This proposed method has been shown to save a lot of computational efforts by eliminating the training procedure of meta-learners. Besides this, the authors also demonstrated that Condorcet’s Jury Theorem holds while ensembling the N number of classifiers in Neural Networks. Our proposed method and experimental results outperformed compared to the state-of-the-art with the optimized ensemble model showing an accuracy of 99.78% and Condorcet’s Jury Theorem-based ensemble model 99.88% on 5-class classification.