Feature Extraction and Diagnosis of Dementia using Magnetic Resonance Imaging
- Authors: Praveen Gupta1, Nagendra Kumar2, Ajad3, N. Arulkumar4, Muthukumar Subramanian5
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View Affiliations Hide Affiliations1 Department of Computer Science and Engineering, GITAM School of Technology, GITAM (Deemed to be University), Visakhapatnam (Andhra Pradesh) India-530045 2 Department of Electronics and Communication Engineering, S J C Institute of Technology, Chickballapur, Karnataka-562101, India 3 Department of Electronics and Communication Engineering, S J C Institute of Technology, Chickballapur, Karnataka-562101, India 4 Department of Statistics and Data Science, CHRIST (Deemed to be University) Bangalore, Karnataka, India - 560029 5 SRM Institute of Science & Technology, Tiruchirappalli Campus, (Deemed to be University), Trichy, Tamilnadu, India - 621105
- Source: AI and IoT-based intelligent Health Care & Sanitation , pp 159-175
- Publication Date: April 2023
- Language: English
Feature Extraction and Diagnosis of Dementia using Magnetic Resonance Imaging, Page 1 of 1
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Dementia is a state of mind in which the sufferer tends to forget important data like memories, language, etc.. This is caused due to the brain cells that are damaged. The damaged brain cells and the intensity of the damage can be detected by using Magnetic Resonance Imaging. In this process, two extraction techniques, Gray Level Co-Occurrence Matrix (GLCM) and the Gray Level Run-Length matrix (GLRM), are used for the clear extraction of data from the image of the brain. Then the data obtained from the extraction techniques are further analyzed using four machine learning classifiers named Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and the combination of two classifiers (SVM+KNN). The results are further analyzed using a confusion matrix to find accuracy, precision, TPR/FPR - True and False Positive Rate, and TNR/FNR – True and False Negative Rate. The maximum accuracy of 93.53% is obtained using the GLRM Feature Extraction (FE) technique with the combination of the SVM and KNN algorithm.
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