Similarity ELREI: Ensemble Learning of ResNet, EfficientNet, and Inception-v3 for Lung Disease Classification based on Chest X-Ray Image

Ermatita, Ermatita (2024) Similarity ELREI: Ensemble Learning of ResNet, EfficientNet, and Inception-v3 for Lung Disease Classification based on Chest X-Ray Image. Turnitin Universitas Sriwijaya. (Submitted)

Text Similarity-ELREI_ Ensemble Learning of ResNet, EfficientNet, and Inception-v3 for Lung Disease Classification based on Chest X-Ray Image.pdf Download (3MB)

Abstract

A Chest X-ray (CXR) image can diagnose lung diseases. However, a diagnosis requires time and high accuracy, so an automatic system is needed. Convolutional neural network (CNN) is a reliable method for image classification and has many architectures. ResNet is a CNN architecture that can overcome gradient vanishing, but it has a deep network structure to detect errors. The EfficientNet CNN architecture can proportionally uniformize all depth, width, and resolution dimensions in each layer as needed, but it takes a long time in training. The Inception-v3 CNN architecture uses Inception blocks by reducing dimensions to small convolutions, but it has larger parameters than other architectures. ELREI is an acronym for ensemble learning of ResNet, EfficientNet, and Inception-v3 with weighted voting. ELREI combines the classification results on the ResNet, EfficientNet, and Inception-v3 architectures to overcome the limitations and combine the advantages of each architecture. ELREI works on the training stage at each epoch rather than the final results of each architecture. In addition to voting, ELREI uses a fully convolutional Network (FCN) at the final stage for the best weight determination and to prevent overfitting during training. The results of the accuracy, precision, recall, and F1-score of the ELREI method are excellent, above 98%. The training graph of the ELREI ensemble method proves that ELREI can overcome overfitting that occurs on the architectures. The results show the Ensemble ELREI method is excellent and robust for lung disease classification based on CXR images, which are carried out in 4 classes: normal, COVID-19, lung opacity, and pneumonia.

Item Type:	Other
Subjects:	#3 Repository of Lecturer Academic Credit Systems (TPAK) > Results of Ithenticate Plagiarism and Similarity Checker
Divisions:	09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User:	Dr Ermatita zuhairi
Date Deposited:	25 Jun 2024 05:55
Last Modified:	25 Jun 2024 05:55
URI:	http://repository.unsri.ac.id/id/eprint/147662

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