Chest X-ray and CT Scan Classification using Ensemble Learning through Transfer Learning
ICST Transactions on Scalable Information Systems
; 2022.
Article
in English
| Web of Science | ID: covidwho-2090586
ABSTRACT
COVID-19 has posed an extraordinary challenge to the entire world. As the number of COVID-19 cases continues to climb around the world, medical experts are facing an unprecedented challenge in correctly diagnosing and predicting the disease. The present research attempts to develop a new and effective strategy for classifying chest X-rays and CT Scans in order to distinguish COVID-19 from other diseases. Transfer learning was used to train various models for chest X-rays and CT Scan, including Inceptionv3, Xception, InceptionResNetv2, DenseNet121, and Resnet50. The models are then integrated using an ensemble technique to improve forecast accuracy. The proposed ensemble approach is more effective in classifying X-ray and CT Scan and forecasting COVID-19.
Full text:
Available
Collection:
Databases of international organizations
Database:
Web of Science
Language:
English
Journal:
ICST Transactions on Scalable Information Systems
Year:
2022
Document Type:
Article
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