Design of deep ensemble classifier with fuzzy decision method for biomedical image classification

Research on biomedical science has many components like biomedical engineering, biomedical signal processing, gene analysis, and biomedical image processing. Classification, detection, and recognition have a great value for disease diagnosis and analysis. In this work, biomedical image classification is discussed. In one part, the brain tumor is considered with brain magnetic resonance images and in the other part, COVID affected chest X-rays have been classified using the ensemble approach. The images have been collected from Kaggle online platform. For this purpose, four heterogeneous base classifiers as Convolutional Neural Network, Recurrent Neural Network, Long Short Term Memory, and Gated Recurrent Unit are considered, and metadata is generated. Further, for the detection purpose, a fuzzy min-max model is utilized to avoid uncertainty. The ensemble output from the base classifiers is fed to the fuzzy model in terms of class probability and labels. The min-max algorithm for correct decisions is used in the fuzzy model. The measuring parameters like precision, recall, accuracy, sensitivity, specificity, and F1-score are evaluated. 100% training accuracy for both the datasets is obtained whereas 97.62% and 95.24% of validation accuracy are found for brain image and chest X-ray image classification respectively as exhibited in the result section. (C) 2021 Elsevier B.V. All rights reserved.