TY - GEN
T1 - Normal/Abnormal Magnetic Resonant Brain Images Detection Based on Supervised Classifiers
AU - Ahmad, Faiza
AU - Hameed, Zia
AU - Rehman, Saifur
AU - Ghafoor, Zubair
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/9
Y1 - 2020/9/9
N2 - Automatic classification of brain images as normal or abnormal is imperative for medical analysis and to save hospital resources. Computational methods can provide more information which cannot be attained by visual interpretation. Due to this, a lot of research has been targeted using computer vision to do medical image analysis. In this research, seven brain diseases are used to consider as abnormal brain condition, which include Glioma, Pick's disease, Meningioma, Huntington, Alzheimer, Sarcoma and Alzheimer's disease plus visual agnosia respectively. The dataset has been taken from the website of Harvard Medical School which contains 160 images. A novel combination of supervised classifiers has been used to segregate the MRI brain images. The proposed research will assist the doctors in the detection process of brain image as normal or abnormal for the aforementioned brain diseases.
AB - Automatic classification of brain images as normal or abnormal is imperative for medical analysis and to save hospital resources. Computational methods can provide more information which cannot be attained by visual interpretation. Due to this, a lot of research has been targeted using computer vision to do medical image analysis. In this research, seven brain diseases are used to consider as abnormal brain condition, which include Glioma, Pick's disease, Meningioma, Huntington, Alzheimer, Sarcoma and Alzheimer's disease plus visual agnosia respectively. The dataset has been taken from the website of Harvard Medical School which contains 160 images. A novel combination of supervised classifiers has been used to segregate the MRI brain images. The proposed research will assist the doctors in the detection process of brain image as normal or abnormal for the aforementioned brain diseases.
KW - Biogeography based optimized Support vector Machine (BBO SVM)
KW - Kernel Support vector machine (KSVM)
KW - Magnetic Resonance Imaging (MRI)
KW - Principal Component Analysis (PCA)
UR - http://www.scopus.com/inward/record.url?scp=85098451665&partnerID=8YFLogxK
U2 - 10.1109/ICCIT-144147971.2020.9213711
DO - 10.1109/ICCIT-144147971.2020.9213711
M3 - Conference Proceeding
AN - SCOPUS:85098451665
T3 - 2020 International Conference on Computing and Information Technology, ICCIT 2020
BT - 2020 International Conference on Computing and Information Technology, ICCIT 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Computing and Information Technology, ICCIT 2020
Y2 - 9 September 2020 through 10 September 2020
ER -