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❯Prediction of Multidrug-Resistant TB from CT Pulmonary Images Based on Deep Learning Techniques
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Prediction of Multidrug-Resistant TB from CT Pulmonary Images Based on Deep Learning Techniques
For:
Clinicians and medical professionals predicting multidrug-resistant (MDR) patients from drug-sensitive (DS) ones based on CT lung images.
Goal:
Improved Employee Efficiency
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Prediction of Multidrug-Resistant TB from CT Pulmonary Images Based on Deep Learning Techniques
For:
Clinicians and medical professionals predicting multidrug-resistant (MDR) patients from drug-sensitive (DS) ones based on CT lung images.Goal:
Improved Employee EfficiencyProblem addressed
Some versions, or abnormities, of tuberculosis are resistant to multiple drugs used to treat this disease/infection. Although these abnormities can sometimes be observed and referenced to in CT Pulmonary scans by professional clinicians (doctors), they are sometimes not obvious.
The conventional diagnostic procedure comprises applying microbiological culture that takes several weeks and remains expensive.
Description
To complement the conventional diagnostic procedure, high resolution computer tomography (CT) of pulmonary images has been resorted to not only for aiding clinicians to expedite the process of diagnosis but also for monitoring prognosis when administering antibiotic drugs.
As a result, the proposed architecture of CNN + SVM + patch performs the best with classification accuracy rate at 91.11%, on these image scans.
Image
AI: Perceive
Machine Learning
AI: Understand
Diagnose
AI: Act