Improving Medical Image Classification Using Ensemble Learning and Deep Convolutional Neural Networks

Abstract

Classification of medical images is essential for helping physicians make correct diagnoses and treatment choices. The performance of Deep Convolutional Neural Networks (CNNs) in a variety of image classification tasks has been exemplary. Single CNN models might not be able to capture all the subtleties inherent in the data due to the intricate and varied nature of medical imaging. In this paper, using the strength of ensemble learning and deep CNNs, we suggest a novel method to improve medical image classification. Our approach entails building an ensemble model out of several deep CNN architectures, each of which was trained using a portion of the medical image dataset. We seek to increase classification accuracy and robustness by using the diversity of these models. The ensemble model combines predictions from different CNNs using methods like bagging and boosting to provide a more thorough and trustworthy classification result. We run comprehensive tests on several medical imaging datasets to verify the efficiency of our suggested approach. Our ensemble learning architecture regularly outperforms single CNN models in terms of accuracy, sensitivity, and specificity. Additionally, we offer details on how the ensemble size, diversity of the constituent models, and other crucial elements affect the performance of the system.