A three layer stacked multimodel transfer learning approach for deep feature extraction from Chest Radiographic images for the classification of COVID-19

Abstract

COVID-19 has had a profound impact on global health, targeting the human respiratory system and causing significant disruptions to human life. To aid in effective diagnosis and classification, numerous machine learning and deep learning models have been developed to analyse limited Chest Radiographic and Computed Tomography images. In this study, we propose a three-layer stacked multimodal approach for deep feature extraction from a high volume of COVID-19 Chest Radiographic images. The proposed model utilizes eight transfer learning models pre-trained on the ImageNet dataset, evaluated based on key performance metrics such as accuracy, precision, and recall. The unique stacking model integrates the outputs of these transfer learning models, extracting deep features through a three-layer architecture. These features are flattened, concatenated, and passed through seven deep dense layers with varying kernel and bias dimensions to achieve optimal classification performance. The approach was applied to a large COVID-19 Chest Radiographic images dataset, achieving the highest accuracy (95.79%), precision (95.44%), and recall (96.65%) when compared to state-of-the-art models. This study demonstrates the effectiveness of leveraging a stacked transfer learning multimodal framework for COVID-19 diagnosis. The proposed method not only ensures high accuracy but also provides a computationally efficient solution for analysing large-scale radiographic datasets, positioning it as a robust tool for aiding in the early detection and classification of COVID-19.