Classification of Pulmonary Diseases Using a Deep Learning Stacking Ensemble Model
Abstract
This paper presents our research in the area of medical imaging diagnostics, focusing specifically on countering the devastating impact of the COVID-19 pandemic and numerous pulmonary pathologies. Using new deep-learning approaches and techniques, we aim to create an advanced classification tool that will be able to capture complex patterns and features in chest image data. This paper introduces the use of state-of-the-art strategies, such as stacked ensemble models, transfer learning, and artificial neural networks, to build a model with unprecedented precision, recall, F1-score, and accuracy. The core idea of our research is to combine different convolutional neural network architectures to bring together their best extraction and classification qualities. The combination of DenseNet, Xception and Inception achieves the best performance and provides the most reliable classification tool. We also use transfer learning to quickly train our model and optimize generalization, making it suitable for the detection of multiple pulmonary pathologies, including COVID-19. Our model also includes an artificial neural network, trained as a meta-learner, which processes the outputs of the CNNs to make classification decisions. We have thoroughly validated and optimized the meng-learner to improve the model’s accuracy on diagnostic images. The provided paper proposed the successful merge of cutting-edge deep-learning methodologies and image-processing algorithms with the medical imaging industry’s specifics. We aim to disrupt the pulmonary disease diagnosis field with our model, offering medical institutions a reliable tool to fight the current and future threats and challenges posed by COVID-19.
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DOI: https://doi.org/10.31449/inf.v48i14.6145
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