Year: 2024 | Month: January | Volume: 14 | Issue: 1 | Pages: 201-213
DOI: https://doi.org/10.52403/ijhsr.20240124
A Review on Ensemble Machine and Deep Learning Techniques Used in the Classification of Computed Tomography Medical Images
Sheik Imran1, Pradeep N2
1Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India.
2Department of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India.
Corresponding Author: Sheik Imran
ABSTRACT
Ensemble learning combines multiple base models to enhance predictive performance and generalize better on unseen data. In the context of Computed Tomography (CT) image processing, ensemble techniques often leverage diverse machine learning or deep learning architectures to achieve the best results. Ensemble machine learning and deep learning techniques have revolutionized the field of CT image processing by significantly improving accuracy, robustness, and efficiency in various medical imaging tasks. These methods have been instrumental in tasks such as image reconstruction, segmentation, classification, and disease diagnosis. The ensemble models can be divided into those based on decision fusion strategies, bagging, boosting, stacking, negative correlation, explicit/implicit ensembles, homogeneous/heterogeneous ensembles, and explicit/implicit ensembles. In comparison to shallow or traditional, machine learning models and deep learning architectures are currently performing better. Also, a brief discussion of the various ensemble models used in CT images is provided. We wrap up this work by outlining a few possible avenues for further investigation.
Key words: Computed Tomography, Ensemble, Deep learning, Machine Learning.