1. Automatic Detection of Brain Tumor on Computed Tomography Images for Patients in the Intensive Care Unit.
- Author
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Fahmi F, Apriyulida F, Nasution IK, and Sawaluddin
- Subjects
- Algorithms, Brain diagnostic imaging, Humans, Image Processing, Computer-Assisted methods, Intensive Care Units, Neuroimaging instrumentation, Neuroimaging methods, ROC Curve, Reproducibility of Results, Software, Support Vector Machine, Brain Neoplasms diagnostic imaging, Critical Care methods, Diagnosis, Computer-Assisted methods, Pattern Recognition, Automated, Tomography, X-Ray Computed methods
- Abstract
Patients in the intensive care unit require fast and efficient handling, including in-diagnosis service. The objectives of this study are to produce a computer-aided system so that it can help radiologists to classify the types of brain tumors suffered by patients quickly and accurately; to build applications that can determine the location of brain tumors from CT scan images; and to get the results of the analysis of the system design. The combination of the zoning algorithm with Learning Vector Quantization can increase the speed of computing and can classify normal and abnormal brains with an average accuracy of 85%., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2020 Fahmi Fahmi et al.)
- Published
- 2020
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