Akciǧer Hastaliklarinin Teşhisi için Hiyerarşik Siniflamaya Dayali Bir Yöntem
Electrical-Electronics and Biomedical Engineering Conference, ELECO 2024 - Proceedings, Page: 1-5
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
In this study, a model was developed to diagnose healthy individuals and those with COVID-19 or other viral pneumonia respiratory diseases using hierarchical classification. COVID-19 X-ray images were used, and the VGG19 convolutional neural network (CNN) was employed for classification tasks on these images. The main objective is to achieve high accuracy in distinguishing between healthy and sick cases, followed by detailed classification of disease types. The hierarchical model consists of a primary classifier to separate healthy and sick samples, followed by a sub-classifier that differentiates between COVID-19 and other viral pneumonia cases. Minimal preprocessing steps were applied to achieve high accuracy through this multi-layered classification system, yielding 100% accuracy in distinguishing healthy and sick cases, 97.5% accuracy in sub-classification of diseases, and an overall accuracy of 98.94% for the hierarchical model. These results highlight the potential of hierarchical classification in disease diagnosis using medical imaging.
Bibliographic Details
Institute of Electrical and Electronics Engineers (IEEE)
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