Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
Avg review time: 78 days
Avg accept to publ: 48 days
APC: 300 EUR


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


TRAFFIC STATS

2,498,714 unique visits
994,474 downloads
Since November 1, 2009



Robots online now
Googlebot


SCOPUS CiteScore

SCOPUS CiteScore


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 24 (2024)
 
     »   Issue 1 / 2024
 
 
 Volume 23 (2023)
 
     »   Issue 4 / 2023
 
     »   Issue 3 / 2023
 
     »   Issue 2 / 2023
 
     »   Issue 1 / 2023
 
 
 Volume 22 (2022)
 
     »   Issue 4 / 2022
 
     »   Issue 3 / 2022
 
     »   Issue 2 / 2022
 
     »   Issue 1 / 2022
 
 
 Volume 21 (2021)
 
     »   Issue 4 / 2021
 
     »   Issue 3 / 2021
 
     »   Issue 2 / 2021
 
     »   Issue 1 / 2021
 
 
  View all issues  


FEATURED ARTICLE

Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
Issue 2/2022

AbstractPlus






LATEST NEWS

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

Read More »


    
 

  3/2020 - 7

 HIGH-IMPACT PAPER 

Diagnosis of Alzheimer's Disease from Brain Magnetic Resonance Imaging Images using Deep Learning Algorithms

SUGANTHE, R. C. See more information about SUGANTHE, R. C. on SCOPUS See more information about SUGANTHE, R. C. on IEEExplore See more information about SUGANTHE, R. C. on Web of Science, LATHA, R. S. See more information about  LATHA, R. S. on SCOPUS See more information about  LATHA, R. S. on SCOPUS See more information about LATHA, R. S. on Web of Science, GEETHA, M. See more information about  GEETHA, M. on SCOPUS See more information about  GEETHA, M. on SCOPUS See more information about GEETHA, M. on Web of Science, SREEKANTH, G. R. See more information about SREEKANTH, G. R. on SCOPUS See more information about SREEKANTH, G. R. on SCOPUS See more information about SREEKANTH, G. R. on Web of Science
 
View the paper record and citations in View the paper record and citations in Google Scholar
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,411 KB) | Citation | Downloads: 1,072 | Views: 2,298

Author keywords
artificial intelligence, artificial neural network, image classification, machine learning, medical diagnosis

References keywords
disease(23), alzheimer(22), neural(7), prediction(6), learning(6), networks(5), diagnosis(5), deep(5), convolutional(5), techniques(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-08-31
Volume 20, Issue 3, Year 2020, On page(s): 57 - 64
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.03007
Web of Science Accession Number: 000564453800007
SCOPUS ID: 85090360650

Abstract
Quick view
Full text preview
Alzheimer's disease is one amongst the progressive disorder that cruelly affects the brain cells. It causes the death of nerve cells and tissue loss in brain. It usually tends to start slowly and aggravates overtime. The symptoms of Alzheimer's disease vary from person to person depending on the severity of the unhealthiness. It exhibits behavioral symptoms such as communication impairments, memory loss, taking a longer time to complete usual activities, and change in attitude and behavior. If the problem worsens over time, then it cannot be cured. Hence it should be identified at the earlier stage itself and treat the patient to lead a normal life on their own. Deep learning algorithms exhibit marvelous performance over conventional machine learning algorithms in identifying the complex patterns in the large volumes of high-dimensional medical imaging data. Hence, recently significant attention has been paid to apply deep learning for medical diagnosis. In this research, Deep Convolution Neural Network (DCNN) and VGG-16 inspired CNN (VCNN) models have been built to classify the different stages of Alzheimer's Disease from the Magnetic Resonance Imaging(MRI) images. Experiments are carried out on an ADNI dataset and the results obtained show that the proposed models achieved excellent accuracy.


References | Cited By  «-- Click to see who has cited this paper

[1] C. Stolojescu-Crisan, S. Holban, "A comparison of x-ray image segmentation techniques," Advances in Electrical and Computer Engineering, vol.13, no.3, pp.85-92, 2013,
[CrossRef] [Full Text] [Web of Science Times Cited 37] [SCOPUS Times Cited 58]


[2] A. V. Lebedev, E. Westman, G. J. P. Van Westen, M. G. Kramberger, A. Lundervold, D. Larsland, et al., "Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness", NeuroImage: Clinical Journal, vol. 6, pp. 115-125, 2014,
[CrossRef] [Web of Science Times Cited 181] [SCOPUS Times Cited 222]


[3] U. R. Acharya, S. L. Fernandes, J. E. Wei Koh, E. J. Ciaccio, M. K. M. Fabell, U. J. Tanik et al. ,"Automated detection of Alzheimer's disease using brain MRI images- A study with various feature extraction techniques" Journal of Medical Systems, vol.43, no.9, pp.1-8,2019,
[CrossRef] [Web of Science Times Cited 131] [SCOPUS Times Cited 209]


[4] Mohamed M. Dessouky and Mohamed A. Elrashidy "Feature extraction of the Alzheimer's disease images using different optimization algorithms," Journal of Alzheimer's Disease & Parkinsonism , vol.6, no.2, pp.1-11, 2016,
[CrossRef]


[5] S. Liu, S. Liu, W. Cai, S. Pujol, R. Kikinis and D. Feng, "Early diagnosis of alzheimer's disease with deep learning," 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), Beijing,July 31, pp. 1015-1018, 2014,
[CrossRef] [SCOPUS Times Cited 407]


[6] J. Dukart, K. Mueller, H. Barthel, A. Villringer, O. Sabri, M. L. Schroeter,"Meta-analysis based SVM classification enables accurate detection of alzheimer's disease across different clinical centers using FDG-PET and MRI," Psychiatry research, vol. 212, no.3, pp. 230-236, 2013,
[CrossRef] [Web of Science Times Cited 94] [SCOPUS Times Cited 110]


[7] K. Ota, N. Oishi, K. Ito, H. Fukuyama , J. Sead, Study Group & Alzheimer's Disease Neuroimaging Initiative, "Effects of imaging modalities, brain atlases and feature selection on prediction of Alzheimer's disease," Journal of Neurosci Methods, vol.256, pp.168-183,2015,
[CrossRef] [Web of Science Times Cited 45] [SCOPUS Times Cited 52]


[8] F. Malik, S. Farhan, M. A. Fahiem, "An ensemble of classifiers based approach for prediction of Alzheimer's disease using fMRI images based on fusion of volumetric, textural and hemodynamic features," Advances in Electrical and Computer Engineering, vol.18, no.1, pp.61-70, 2018,
[CrossRef] [Full Text] [Web of Science Times Cited 6] [SCOPUS Times Cited 8]


[9] A. Ayub, S. Farhan, M. A. Fahiem, H. Tauseef, "A novel approach for the prediction of conversion from mild cognitive impairment to Alzheimer's disease using MRI images," Advances in Electrical and Computer Engineering, vol.17, no.2, pp.113-122, 2017,
[CrossRef] [Full Text] [Web of Science Times Cited 3] [SCOPUS Times Cited 4]


[10] O. Geman, C. O. Turcu, A. Graur, "Parkinson's disease assessment using fuzzy expert system and nonlinear dynamics," Advances in Electrical and Computer Engineering, vol.13, no.1, pp.41-46, 2013,
[CrossRef] [Full Text] [Web of Science Times Cited 26] [SCOPUS Times Cited 26]


[11] J. Weller, & A. Budson, "Current understanding of Alzheimer's disease diagnosis and treatment," F1000 Research, 7,2018,
[CrossRef] PMID: 30135715; PMCID: PMC6073093 [SCOPUS Times Cited 596]


[12] M. W. Bondi, E. C. Edmonds, Salmon DP. "Alzheimer's disease: past, present, and future", J Int Neuropsychol Soc, vol.23(9-10), pp.818-831, 2017,
[CrossRef] [Web of Science Times Cited 322] [SCOPUS Times Cited 379]


[13] B. Dubois, A. Padovani, P.Scheltens, A. Rossi & G.Dell'Agnello, "Timely diagnosis for alzheimer's disease: A literature review on benefits and challenges", Journal of Alzheimer's Disease, vol. 49, no. 3, pp. 617-631, 2016
[CrossRef] [Web of Science Times Cited 303] [SCOPUS Times Cited 321]


[14] M. Tanveer, B. Richhariya, R. U. Khan, A. H. Rashid, P. Khanna, M. Prasad et al., "Machine learning techniques for the diagnosis of Alzheimer's disease: A review," ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol.16,no.1S, pp.1-35, 2020,
[CrossRef] [Web of Science Times Cited 170] [SCOPUS Times Cited 226]


[15] C. K. Fisher, A. M. Smith, J. R. Walsh,"Machine learning for comprehensive forecasting of Alzheimer's disease progression," Science Reports 9, vol.13622, 2019,
[CrossRef] [Web of Science Times Cited 89] [SCOPUS Times Cited 113]


[16] J. De Velasco Oriol, E. E. Vallejo, K. Estrada, G. Tamez Pena and ADNI, "Benchmarking machine learning models for late-onset alzheimer's disease prediction from genomic data," BMC Bioinformatics 20, vol.709, 2019,
[CrossRef] [Web of Science Times Cited 35] [SCOPUS Times Cited 45]


[17] L. R. Trambaiolli, A. C. Lorena, F. J. Fraga, , P. A. M. Kanda, R. Anghinah & R. Nitrini, "Improving alzheimer's disease diagnosis with machine learning techniques," Clinical EEG and Neuroscience, vol.42, no.3, pp.160-165, 2011,
[CrossRef] [Web of Science Times Cited 86] [SCOPUS Times Cited 105]


[18] S. Basaia, F. Agosta, L. Wagner, E. Canu, G. Magnani, R. Santangelo et al., and Alzheimer's Disease Neuroimaging Initiative, "Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks," NeuroImage: Clinical Journal,vol.21,no.1, pp.1-8, 2019,
[CrossRef] [Web of Science Times Cited 306] [SCOPUS Times Cited 395]


[19] O. Geman, H. Costin, "Automatic assessing of tremor severity using nonlinear dynamics, artificial neural networks and neuro-fuzzy classifier," Advances in Electrical and Computer Engineering, vol.14, no.1, pp.133-138, 2014,
[CrossRef] [Full Text] [Web of Science Times Cited 28] [SCOPUS Times Cited 28]


[20] Y. LeCun, Y. Bengio, G. Hinton "Deep learning," Nature, vol.521, no.1, pp.436-444,2015,
[CrossRef] [Web of Science Times Cited 21681] [SCOPUS Times Cited 53255]


[21] A. Krizhevsky, I. Sutskever, G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Communication of the ACM, vol.60, no.6, pp.84-90,2017,
[CrossRef] [SCOPUS Times Cited 17234]


[22] N. Sharma, V. Jain & A. Mishra, "An analysis of convolutional neural networks for image classification," Procedia Computer Science,vol.132, pp.377-384, 2018,
[CrossRef] [SCOPUS Times Cited 344]


[23] U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, H. Adeli, D. P. Subha "Automated EEG-based screening of depression using deep convolutional neural network," Computer Methods and Programs in Biomedicine, vol.161, pp.103-113, 2018,
[CrossRef] [Web of Science Times Cited 333] [SCOPUS Times Cited 402]


[24] S. E. Spasov, L. Passamonti, A. Duggento, P. Lio, N. Toschi , "A multi-modal convolutional neural network framework for the prediction of Alzheimer's disease," Conf Proc IEEE Eng Med Biol Soc. 2018, pp.1271‐1274, 2018,
[CrossRef] [SCOPUS Times Cited 44]


[25] A. Payan, G. Montana, "Predicting Alzheimer's disease: A neuroimaging study with 3D convolutional neural networks," arXiv, February 10, 2015, arXiv:1502.02506

[26] J. Nowaková, M. Prilepok & V. Snášel, "Medical image retrieval using vector quantization and fuzzy S-tree," Journal of Medical Systems, vol. 41, no. 18, pp. 1-16, 2017,
[CrossRef] [Web of Science Times Cited 64] [SCOPUS Times Cited 73]


[27] Elena-Lorena Hedrea, Radu-Emil Precup, Claudia-Adina Bojan-Dragos, "Results on tensor product-based model transformation of magnetic levitation systems," Acta Polytechnica Hungarica, vol. 16, no. 9, pp. 93-111, 2019.

[28] R. P. Gil, Z. C. Johanyák & T. Kovács, "Surrogate model based optimization of traffic lights cycles and green period ratios using microscopic simulation and fuzzy rule interpolation," International journal of artificial intelligence, vol.16, pp.20-40,2018, Corpus ID: 53310666

[29] T. Haidegger, L. Kovacs, S. Preitl, R. E. Precup, B. Benyo, Z. Benyo, "Controller design solutions for long distance telesurgical applications," International Journal of Artificial Intelligence, vol. 6, no.11S , pp. 48-71,2011.

[30] https://fsl.fmrib.ox.ac.uk/fsl/fslwiki

[31] M. Jenkinson, C. Beckmann, T. E. J. Behrens, M. W. Woolrich, S. M. Smith, "FSL," NeuroImage, vol.62, no.2, pp.782-790, 2012,
[CrossRef] [Web of Science Times Cited 7001] [SCOPUS Times Cited 7229]




References Weight

Web of Science® Citations for all references: 30,941 TCR
SCOPUS® Citations for all references: 81,885 TCR

Web of Science® Average Citations per reference: 967 ACR
SCOPUS® Average Citations per reference: 2,559 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-03-28 22:36 in 150 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2024
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.




Website loading speed and performance optimization powered by: 


DNS Made Easy