Abstract
The physical appearance of a brain tumor in human beings may be an indication of problems in psychological (cognitive) functions. Such functions include learning, understanding, problem solving, decision making, and planning. Early brain tumor detection can be done by using the proper procedure of screening. MRI is used for the detection of disease staging and follow-up without ionization radiation. In this manuscript, an automated system is proposed for the analysis of brain data and detection of cognitive functions abnormalities. The region of interest (ROI) is enhanced using a proposed partial differential diffusion filter (PDDF) which is a modified form of anisotropic diffusion filter. Otsu algorithm is used for better segmentation. Moreover, a new method is also proposed for feature extraction which is a concatenation of local binary pattern (LBP) and Gray level co-occurrence matrix (C2LBPGLCM). The proposed method accurately distinguishes between healthy and unhealthy images with high specificity, sensitivity, and area under the curve.
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Acknowledgements
This work is supported by Department of Computer Science, COMSATS University Islamabad, Wah Campus Pakistan. We are thankful to COMSATS for providing a strong research platform, fully equipped labs and other research facilities to make this work possible.
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Amin, J., Sharif, M., Yasmin, M. et al. Use of machine intelligence to conduct analysis of human brain data for detection of abnormalities in its cognitive functions. Multimed Tools Appl 79, 10955–10973 (2020). https://doi.org/10.1007/s11042-019-7324-y
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DOI: https://doi.org/10.1007/s11042-019-7324-y