Gaussian model based hybrid technique for infection level identification in TB diagnosis
| Autor: | W. R. Sam Emmanuel, K. S. Mithra |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Infection level
Bacilli Tuberculosis General Computer Science Mean squared error Computer science Gaussian 0206 medical engineering Decision tree Deep Belief Networks 02 engineering and technology TB diagnosis symbols.namesake Deep belief network 0202 electrical engineering electronic engineering information engineering medicine Sputum smear images biology business.industry Decision Trees Pattern recognition QA75.5-76.95 medicine.disease Mixture model biology.organism_classification 020601 biomedical engineering Gaussian mixture model Electronic computers. Computer science symbols 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) |
| Zdroj: | Journal of King Saud University: Computer and Information Sciences, Vol 33, Iss 8, Pp 988-998 (2021) |
| ISSN: | 1319-1578 |
| DOI: | 10.1016/j.jksuci.2018.07.008 |
| Popis: | Tuberculosis (TB) is caused by mycobacterium tuberculosis, which is a common disease all over the world that can be deadliest if not diagnosed at the early stages. Thus an accurate and effective technique is required for the diagnosis of TB. Accordingly, a hybrid classifier, named, Gaussian Decision Tree based Deep Belief Network (GDT-DBN) is proposed to diagnose the infection level of TB from the sputum smear microscopic images. Here, a two-level classification is performed using proposed GDT-DBN classifier, which is the combination of Decision Tree (DT), Deep Belief Network (DBN), and Gaussian Mixture Model (GMM). The first level classification depends on categorizing the image into three classes, namely few bacilli, no bacilli and overlapping bacilli, whereas the second level classification finds the number of bacilli present and based on the bacilli count, the density ratio is measured to determine the infection level. The results for Mean square error, Missing count and Infection level difference were calculated and compared which is better than the existing methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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