Multi-Class Flat Classification of Lung Diseases Utilizing Deep Learning
1st IEEE IAS Global Conference on Emerging Technologies, GlobConET 2022
; : 804-809, 2022.
Article
in English
| Scopus | ID: covidwho-2063232
ABSTRACT
Early diagnosis of diseases is very critical for recovery. However, this is not always feasible due to the limited available staff or expensive and inadequate tools as we have witnessed in the recent COVID-19 pandemic. Lung diseases are life-threatening, but fortunately, they can be detected from X-ray images, which are cost-effective approaches. However, they need experts who are sometimes unavailable. Thus, using cutting-edge technology to diagnose diseases automatically and fast is the key solution to saving people's lives. In this research, deep learning techniques have been utilized to classify several lung diseases in a cost-saving, time-saving, and efficient manner. Examples of lung diseases studied in this research are COVID-19, Lung Opacity, Pneumonia, and Tuberculosis. Several pre-trained deep learning models have been employed for flat multi-class classification of these lung diseases instead of using binary classification to recognize one disease from normal cases, as most state-of-the-art studies carry out. The models' performance has been evaluated on imbalanced data of X-ray images with various resolutions and types. Finally, multiple measurements metrics have been utilized to evaluate the performance. The best accuracy achieved in this research is 95.643%. © 2022 IEEE.
COVID-19; deep learning; lung disease; multi-class classification; X-ray images; Biological organs; Classification (of information); Computer aided diagnosis; Cost effectiveness; Image classification; Learning systems; Cost-effective approach; Cutting edge technology; Diagnose disease; Diagnoses of disease; Early diagnosis; IS costs; Key solution; X-ray image
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
1st IEEE IAS Global Conference on Emerging Technologies, GlobConET 2022
Year:
2022
Document Type:
Article
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