Abstract
Dementia is a broad category of brain-related diseases that continues for a long term and severely affects thinking and daily functioning of a human being. Among different types of dementia the fatal type of brain problems are Alzheimer’s Disease (AD) and Parkinson’s Disease (PD). More than 70% of cases are reported as dementia is in the Alzheimer’s category. In AD, the patient’s brain gets severely damaged, especially the outer part of the brain like cerebral cortex, hippocampus, ventricles, etc. The AD patients have enlarged ventricles, shrinkage in hippocampus and cortex. PD is also a common dementia after AD. In PD, the patient’s mid-brain gets damaged, i.e., substantia nigra. The proposed work presents an efficient automation for the detection of the AD and PD with Machine Learning Techniques (MLT). To detect the presence of PD and AD, two different types of brain image databases have to be selected: Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) database images, both of them contain data for AD and PD patients in comparison with the healthy brain images. From the input image, different features have to be extracted like statistical moments, geometrical moments, texture features, etc. Then Region of Interest (ROI) has to be selected to differentiate disease-affected areas. The results have to be generated automatically by comparing input image with the trained samples in the database. The proposed system concentrates on applying the MLT for segregating the outer part of brain with central part of brain for diagnosing the AD and PD in comparison with the healthy brain data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Petrella JR, Coleman R, Doraiswamy P (2003) Neuroimaging and early diagnosis of Alzheimer disease: a look to the future. Radiology 226:315–336
Smith SL, Lones MA, Bedder M, Alty JE, Cosgrove J, Maguire RJ, Pownall ME, Ivanoiu D, Lyle C, Cording A, Elliott CJH (2015) Computational approaches for understanding the diagnosis and treatment of Parkinson’s disease. IET Syst Biol 9(6):226–233
Tripoliti EE, Fotiadis DI, Manis G (2012) Automated diagnosis of diseases based on classification: dynamic determination of the number of trees in random forests algorithm. IEEE Trans Inf Technol Biomed 16(4):615–622
Araujo CPS, Báez PG (2012) GaNEn: a new gating neural ensemble for automatic assessment of the severity level of dementia using neuropsychological tests. Lecture notes in computer science, vol 6928, pp 264
Rabeh AB, Benzarti F, Amiri H (2016) Diagnosis of alzheimer diseases in early step using SVM (Support Vector Machine). In: 13th international conference computer graphics, imaging and visualization, pp 364–367
Anyaiwe OED, Wilson GD, Singh GB, Geddes TJ (2017) Weighted manhattan distance classifier, SELDI data for Alzheimer’s disease diagnosis, evolutionary computation, pp 257–262
Iftikhar MA, Idris A (2016) An ensemble classification approach for automated diagnosis of Alzheimer’s disease and mild cognitive impairment. In: 2016 international conference on open source systems and technologies (ICOSST), pp 78–83
Spedding AL, Di Fatta G, Saddyt JD and the Alzheimer’s Disease Neuroimaging Initiative (2015) An LDA and probability-based classifier for the diagnosis of Alzheimer’s disease from structural MRI. In: IEEE international conference on bioinformatics and biomedicine (BTBM), pp 1405–1411
Valli A, Dr. Wiselin Jiji G (2014) Parkinson’s disease diagnosis using image processing techniques a survey. In: IJCSA, vol 4, no 6, pp 57–67
Peng Y, Fang M, Wang C, Xie J (2015) Entropy chain multi-label classifiers for traditional medicine diagnosing Parkinson’s disease. In: 2015 IEEE international conference on bioinforrnatics and biomedicine (BIBM), pp 1722–1724
Al-Fatlawi AH, Jabardi MH, Ling SH (2016) Efficient diagnosis system for Parkinson’s disease using deep belief network. IEEE pp 1324–1330
Wibawa MS, Nugroho HA, Setiawan NA (2015) Performance evaluation of combined feature selection and classification methods in diagnosing Parkinson disease based on voice feature. In: 2015 international conference on science in information technology (ICSITech), pp 126–131
Nancy Noella RS, Sreeja SS (2013) Computer aided diagnosis of Alzheimer’s disease based on NMF-decision tree classifier. In: International conference on innovative trends in computing and technology
Bailey DL (2005) Positron emission tomography. Springer Verlag, New York
Mignotte M, Meunier J (2000) Three-dimensional blind deconvolution of SPECT images. IEEE Trans Biomed Eng 47(2):274–280
Yu C, Liu K-L, Meng W, Wu Z, Rishe N, Member IEEE (2002) A methodology to retrieve text documents from multiple database. In: IEEE transactions on knowledge and data engineering, vol 14, no 6, pp. 13–47
Warneke D, Kao O (2011) Exploiting dyanmic resource allocation for efficient parallel data processing in the cloud. IEEE Trans Parallel Distrib Syst 22(6):985–997
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Nancy Noella, R.S., Priyadarshini, J. (2019). Efficient Computer-Aided Diagnosis of Alzheimer’s Disease and Parkinson’s Disease—A Survey. In: Nath, V., Mandal, J. (eds) Nanoelectronics, Circuits and Communication Systems . Lecture Notes in Electrical Engineering, vol 511. Springer, Singapore. https://doi.org/10.1007/978-981-13-0776-8_5
Download citation
DOI: https://doi.org/10.1007/978-981-13-0776-8_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0775-1
Online ISBN: 978-981-13-0776-8
eBook Packages: EngineeringEngineering (R0)