article · Diagnostics
Diagnosing COVID-19 can be challenging before substantial complications such as lung damage or blood clots occur. To improve identification, this research proposes stacking ensemble deep learning models that evaluate both patient symptoms and chest X-ray images. One model processes symptom data by combining predictions from multi-layer perceptron, recurrent neural network, long short-term memory, and gated recurrent unit architectures into a support vector machine meta-learner. A second ensemble evaluates chest X-ray scans by combining outputs from pre-trained deep learning networks, specifically VGG16, InceptionV3, ResNet50, and DenseNet121, using an identical support vector machine meta-learning approach. Evaluated across two symptom datasets and two chest X-ray datasets, the stacked ensemble models achieved higher diagnostic performance than any of the individual constituent models tested on the same data.
COVID-19 is frequently difficult to identify in its early stages before significant physical damage occurs. Demonstrating that stacked ensemble algorithms outshine standard single-network deep learning models on both symptom records and radiographic images highlights a practical pathway to improve automated clinical decision support, potentially speeding up detection and treatment planning.
This work demonstrates an applied, early-stage algorithmic framework that could be integrated into clinical diagnostic support software. Potential end users include hospital triage staff, radiologists, and healthcare providers assessing suspected COVID-19 cases from symptoms or imaging. However, because the abstract evaluates the models only on existing datasets without clinical trials or operational deployment, the technology remains at an early developmental stage before real-world adoption.
AI-generated from the published abstract. Always read the original work before citing.
The COVID-19 virus is one of the most devastating illnesses humanity has ever faced. COVID-19 is an infection that is hard to diagnose until it has caused lung damage or blood clots. As a result, it is one of the most insidious diseases due to the lack of knowledge of its symptoms. Artificial intelligence (AI) technologies are being investigated for the early detection of COVID-19 using symptoms and chest X-ray images. Therefore, this work proposes stacking ensemble models using two types of COVID-19 datasets, symptoms and chest X-ray scans, to identify COVID-19. The first proposed model is a stacking ensemble model that is merged from the outputs of pre-trained models in the stacking: multi-layer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). Stacking trains and evaluates the meta-learner as a support vector machine (SVM) to predict the final decision. Two datasets of COVID-19 symptoms are used to compare the first proposed model with MLP, RNN, LSTM, and GRU models. The second proposed model is a stacking ensemble model that is merged from the outputs of pre-trained DL models in the stacking: VGG16, InceptionV3, Resnet50, and DenseNet121; it uses stacking to train and evaluate the meta-learner (SVM) to identify the final prediction. Two datasets of COVID-19 chest X-ray images are used to compare the second proposed model with other DL models. The result has shown that the proposed models achieve the highest performance compared to other models for each dataset.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.3390/diagnostics13111968
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
