An efficient stacked ensemble model for the detection of COVID-19 and skin cancer using fused feature of transfer learning and handcrafted methods

Abstract

ABSTRACT Recently, COVID-19 and Skin cancer have been the most common diseases. If they haven’t been treated early, a severe illness may cause in the patients and may lead to death. Several automated detection methods have been explored in the area, but medical sectors need a high-performance CAD system. This work proposed a fused feature extraction technique containing a transfer learning of the DenseNet-169 model and six handcrafted methods to capture richer and more detailed features. Moreover, we propose a unique first-level stacked ensemble technique that considers RF, GBM, LR, SVM, and KNN algorithms with base-learner and meta-learner phases to enhance the performance of the classifier model. We have used two publicly available datasets for evaluation: SARS-CoV-2 CT Scan and ISIC Archive datasets. The result shows that all five implemented algorithms improve their performance in the meta-learner phase on both dataset types. For the SARS-CoV-2 CT Scan dataset, the LR performs better than the others in all evaluation metrics; it scores 99.73% accuracy. In contrast, RF is the highest performer for the ISIC Archive dataset, with 89.09% of accuracy. The comparison shows our proposed method outperforms the previously worked methods by most of the evaluation metrics in both datasets.