Journal of Soft Computing and Information Technology

Journal of Soft Computing and Information Technology

Classification of CXR Images into 6 Types of Pulmonary Diseases Using a Mixture of ELM Based Experts and ILQP

Document Type : Persian Original Article

Authors
Laleh Armi 1
Elham Abbasi 1
Mohammad Javad Ebadi 2
1 Department of Computer Science, Yazd University, Yazd, Iran.
2 Department of Mathematics, Chabahar Maritime University, Chabahar, Iran.
Abstract
The covid-19 is a new virus that causes upper respiratory system and lung infections. Radiographic imaging is used to monitoring various lung diseases and has recently been used to monitoring covid-19 disease, which to detect early and control the outbreak of the disease. Most research in this field has been devoted to articles based on deep learning methods. Due to the high training and testing time of deep learning-based models, in this paper a mixture of ELM based experts with trainable gating network (MEETG) is used. In MEETG, the advantages of ELM are used for designing the structure of ME. The ELM learning process is faster than SVM and MLP and has a better generalizability than them. In continuation, ILQP has been used as texture descriptor for feature extraction and MEETG has been used as a classifier to diagnose six types of lung disease and healthy lung. To evaluate the proposed model, from the RYDLS-20 dataset with unbalanced samples containing chest X-ray images belonging to seven classes including six different viral pneumonia disease lung diseases (Covid-19, SARS, MERS and varicella), bacteria (Streptococcus), fungus (Pneumocystis) and healthy lungs. Our main aim is to achieve the best possible identification for Covid-19 among other types of pneumonia and healthy lungs. Evaluation measures consisting of accuracy, precision, recall, and F-Score have been used to evaluate the proposed model. The experimental results revealed that F-Score for detection of Covid-19 is equal to 0.897 and the average of the total F-Score and the classification accuracy is 0.80 and 97.07%, respectively. The average of total F-Score of the proposed method improves 24%, 43%, 37% and 20% compared with KNN, DT, MLP and ELM, and compared with ensemble learning based method such as ME and bagging is about 19%.
Keywords
Ensemble learning
Mixture of experts
Texture descriptor
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Journal of Soft Computing and Information Technology
Volume 13, Issue 3
Autumn - December
Autumn 2024
Pages 73-87