Detection of Frontotemporal Dementia using Artificial Intelligence Techniques
Sandhya. N.1 , Rama Prasath. A.2
1Sandhya. N., Research Scholar, Department of Computer Applications, Hindustan Institute of Technology & Science, Padur, Tamil Nadu, India.
2Rama Prasath. A., Asst. Professor, Department of Computer Applications, School of Computing Sciences, Hindustan Institute of Technology & Science, Padur, Tamil Nadu, India.
Manuscript received on 10 August 2019. | Revised Manuscript received on 17 August 2019. | Manuscript published on 30 September 2019. | PP: 8587-8590 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6910098319/19©BEIESP | DOI: 10.35940/ijrte.C6910.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Atrophy (degeneration) of frontal and temporal lobes in human brain causes a dysfunctionality in Language, Emotion, Executive abilities and these losses are irreversible. If Atrophy is detected at an early age it is beneficial for the patient. A detailed diagnosis of the patient in terms of physical, physiological, psychological and neuropsychological aspects are mandatory to trace the possibility. There is a continuous increase in population of the disease and limited number of specialists. Frontotemporal Dementia (FTD) happens due to the frontal and/or temporal lobe degeneration. The proposed system helps in the identification of the FTD in the brain using various Artificial Intelligence (AI) techniques. The study makes use of the MR brain images for the purpose of the detection of the FTD.
Keywords: Fronto Temporal Dementia (FTD), Back Propagation Network (BPN), Support Vector Machine (SVM), Naïve Bayes, Gray-Level Co-occurrence Matrix (GLCM).
Scope of the Article: Artificial Intelligence and Machine Learning