Automatic Detection of Brain Tumor on Computed Tomography Images for Patients in the Intensive Care Unit

Joint Authors

Fahmi, Fahmi
Apriyulida, Fitri
Nasution, Irina Kemala
Sawaluddin, Irina Kemala

Source

Journal of Healthcare Engineering

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-07-15

Country of Publication

Egypt

No. of Pages

13

Main Subjects

Public Health
Medicine

Abstract EN

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%.

American Psychological Association (APA)

Fahmi, Fahmi& Apriyulida, Fitri& Nasution, Irina Kemala& Sawaluddin, Irina Kemala. 2020. Automatic Detection of Brain Tumor on Computed Tomography Images for Patients in the Intensive Care Unit. Journal of Healthcare Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1186229

Modern Language Association (MLA)

Fahmi, Fahmi…[et al.]. Automatic Detection of Brain Tumor on Computed Tomography Images for Patients in the Intensive Care Unit. Journal of Healthcare Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1186229

American Medical Association (AMA)

Fahmi, Fahmi& Apriyulida, Fitri& Nasution, Irina Kemala& Sawaluddin, Irina Kemala. Automatic Detection of Brain Tumor on Computed Tomography Images for Patients in the Intensive Care Unit. Journal of Healthcare Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1186229

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1186229