{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T15:18:42Z","timestamp":1787671122096,"version":"build-2736575974"},"reference-count":63,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,24]],"date-time":"2021-01-24T00:00:00Z","timestamp":1611446400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Birkbeck College, University of London","award":["BEI School Grant"],"award-info":[{"award-number":["BEI School Grant"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer\u2019s Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.<\/jats:p>","DOI":"10.3390\/s21030778","type":"journal-article","created":{"date-parts":[[2021,1,25]],"date-time":"2021-01-25T12:28:31Z","timestamp":1611577711000},"page":"778","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4503-615X","authenticated-orcid":false,"given":"Nitsa J.","family":"Herzog","sequence":"first","affiliation":[{"name":"Department of Computer Science, Birkbeck College, University of London, London WC1E 7HZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1884-0772","authenticated-orcid":false,"given":"George D.","family":"Magoulas","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Birkbeck College, University of London, London WC1E 7HZ, UK"},{"name":"Birkbeck Knowledge Lab, University of London, London WC1E 7HZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1007\/s10916-019-1469-0","article-title":"A mobile-based screening system for data analyses of early dementia traits detection","volume":"44","author":"Thabtah","year":"2020","journal-title":"J. Med. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"112821","DOI":"10.1016\/j.eswa.2019.112821","article-title":"A systematic survey of computer-aided diagnosis in medicine: Past and present developments","volume":"138","author":"Yanase","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lazli, L., Boukadoum, M., and Mohamed, O.A. (2020). A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion. Appl. Sci., 10.","DOI":"10.3390\/app10051894"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1038\/nrn1009","article-title":"Mapping brain asymmetry","volume":"4","author":"Toga","year":"2003","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"McManus, C. (2019). Half a century of handedness research: Myths, truths; fictions, facts; backwards, but mostly forwards. Brain Neurosci. Adv., 3.","DOI":"10.1177\/2398212818820513"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1093\/molbev\/msx303","article-title":"Multiple innovations in genetic and epigenetic mechanisms cooperate to underpin human brain evolution","volume":"35","author":"Bitar","year":"2018","journal-title":"Mol. Biol. Evol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Isles, A.R. (2018). Epigenetics, chromatin and brain development and function. Brain Neurosci. Adv., 2.","DOI":"10.1177\/2398212818812011"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1093\/cercor\/bhr230","article-title":"Laterality patterns of brain functional connectivity: Gender effects","volume":"22","author":"Tomasi","year":"2012","journal-title":"Cereb. Cortex"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1037\/0882-7974.17.1.85","article-title":"Hemispheric asymmetry reduction in older adults: The HAROLD model","volume":"17","author":"Cabeza","year":"2002","journal-title":"Psychol. Aging"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1093\/cercor\/bhg133","article-title":"Task-independent and task-specific age effects on brain activity during working memory, visual attention and episodic retrieval","volume":"14","author":"Cabeza","year":"2004","journal-title":"Cereb. Cortex"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kalavathi, P., Senthamilselvi, M., and Prasath, V.B. (2017). Review of computational methods on brain symmetric and asymmetric analysis from neuroimaging techniques. Technologies, 5.","DOI":"10.3390\/technologies5020016"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"261","DOI":"10.3389\/fnagi.2017.00261","article-title":"The abnormality of topological asymmetry between hemispheric brain white matter networks in Alzheimer\u2019s disease and mild cognitive impairment","volume":"9","author":"Yang","year":"2017","journal-title":"Front. Aging Neurosci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3","DOI":"10.3389\/fneur.2018.00003","article-title":"Changes in brain lateralization in patients with mild cognitive impairment and Alzheimer\u2019s disease: A resting-state functional magnetic resonance study from Alzheimer\u2019s disease neuroimaging initiative","volume":"9","author":"Liu","year":"2018","journal-title":"Front. Neurol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1016\/j.neurobiolaging.2011.06.026","article-title":"Cortical asymmetries in normal, mild cognitive impairment, and Alzheimer\u2019s disease","volume":"33","author":"Kim","year":"2012","journal-title":"Neurobiol. Aging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3253","DOI":"10.1093\/brain\/aww243","article-title":"Alzheimer\u2019s Disease Neuroimaging Initiative. Whole-brain analysis reveals increased neuroanatomical asymmetries in dementia for hippocampus and amygdala","volume":"139","author":"Wachinger","year":"2016","journal-title":"Brain"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.neuroimage.2015.01.032","article-title":"Alzheimer\u2019s Disease Neuroimaging Initiative. BrainPrint: A discriminative characterization of brain morphology","volume":"109","author":"Wachinger","year":"2015","journal-title":"NeuroImage"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Despotovi\u0107, I., Goossens, B., and Philips, W. (2015). MRI segmentation of the human brain: Challenges, methods, and applications. Comput. Math. Methods Med.","DOI":"10.1155\/2015\/450341"},{"key":"ref_18","unstructured":"Zheng, A., and Casari, A. (2018). Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists, O\u2019Reilly Media, Inc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s41044-016-0014-0","article-title":"Big data preprocessing: Methods and prospects","volume":"1","author":"Luengo","year":"2016","journal-title":"Big Data Anal."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhou, K., He, W., Xu, Y., Xiong, G., and Cai, J. (2018). Feature selection and transfer learning for Alzheimer\u2019s disease clinical diagnosis. Appl. Sci., 8.","DOI":"10.3390\/app8081372"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.compbiomed.2017.02.011","article-title":"Classification of Alzheimer\u2019s disease and prediction of mild cognitive impairment-to-Alzheimer\u2019s conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm","volume":"83","author":"Beheshti","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ijaz, M.F., Attique, M., and Son, Y. (2020). Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods. Sensors, 20.","DOI":"10.3390\/s20102809"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.future.2020.07.047","article-title":"An intelligent healthcare monitoring framework using wearable sensors and social networking data","volume":"114","author":"Ali","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Khoshgoftaar, T., Dittman, D., Wald, R., and Fazelpour, A. (2012, January 12\u201315). First order statistics based feature selection: A diverse and powerful family of feature seleciton techniques. Proceedings of the 2012 11th International Conference on Machine Learning and Applications (ICMLA 2012), Boca Raton, FL, USA.","DOI":"10.1109\/ICMLA.2012.192"},{"key":"ref_25","unstructured":"Welling, M. (2019, April 23). Fisher Linear Discriminant Analysis|| University of Toronto. Technical Note. Available online: https:\/\/www.cs.huji.ac.il\/~csip\/Fisher-LDA.pdf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2017\/5485080","article-title":"Diagnosis of Alzheimer\u2019s disease based on structural MRI images using a regularized extreme learning machine and PCA features","volume":"1","author":"Lama","year":"2017","journal-title":"J. Healthc. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal Component Analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_28","unstructured":"Glozman, T., and Le, R.K. (2019, April 23). Classification of Alzheimer\u2019s Disease Based on White Matter Architecture. Available online: http:\/\/cs229.stanford.edu\/proj2014\/Tanya%20Glozman,%20Rosemary%20Le,%20Classification%20of%20Alzheimer%27s%20Disease%20Based%20on%20White%20Matter%20Attributes.pdf."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Le, N.Q.K., Do, D.T., Hung, T.N.K., Lam, L.H.T., Huynh, T.T., and Nguyen, N.T.K. (2020). A computational framework based on ensemble deep neural networks for essential genes identification. Int. J. Mol. Sci., 21.","DOI":"10.3390\/ijms21239070"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.neuroimage.2014.10.002","article-title":"Machine learning framework for early MRI-based Alzheimer\u2019s conversion prediction in MCI subjects","volume":"104","author":"Moradi","year":"2015","journal-title":"Neuroimage"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"756","DOI":"10.3389\/fneur.2019.00756","article-title":"A Novel Ensemble-Based Machine Learning Algorithm to Predict the Conversion from Mild Cognitive Impairment to Alzheimer\u2019s Disease Using Socio-demographic Characteristics, Clinical Information and Neuropsychological Measures","volume":"10","author":"Grassi","year":"2019","journal-title":"Front. Neurol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"101645","DOI":"10.1016\/j.nicl.2018.101645","article-title":"Automated classification of Alzheimer\u2019s disease and mild cognitive impairment using a single MRI and deep neural networks","volume":"21","author":"Basaia","year":"2019","journal-title":"Neuroimage Clin."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Stamate, D., Smith, R., Tsygancov, R., Vorobev, R., Langham, J., Stahl, D., and Reeves, D. (2020, January 5\u20137). Applying Deep Learning to Predicting Dementia and Mild Cognitive Impairment. Proceedings of the AIAI International Conference on Artificial Intelligence Applications and Innovations, Halkidiki, Greece.","DOI":"10.1007\/978-3-030-49186-4_26"},{"key":"ref_35","unstructured":"(2020, December 15). AnalyzeDirect. Available online: https:\/\/analyzedirect.com\/analyze14\/."},{"key":"ref_36","unstructured":"(2020, December 15). FreeSurfer. Available online: https:\/\/surfer.nmr.mgh.harvard.edu\/."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ruppert, G.C., Teverovskiy, L., Yu, C.P., Falcao, A.X., and Liu, Y. (April, January 30). A new symmetry-based method for mid-sagittal plane extraction in neuroimages. Proceedings of the 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2011), Chicago, IL, USA.","DOI":"10.1109\/ISBI.2011.5872407"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1109\/42.918469","article-title":"Robust midsagittal plane extraction from normal and pathological 3-D neuroradiology images","volume":"20","author":"Liu","year":"2001","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_39","unstructured":"Teverovskiy, L., and Li, Y. (2006, January 6\u20139). Truly 3D midsagittal plane extraction for robust neuroimage registration. Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, USA."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Michalak, H., and Okarma, K. (2019). Improvement of image binarization methods using image preprocessing with local entropy filtering for alphanumerical character recognition purposes. Entropy, 21.","DOI":"10.3390\/e21060562"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Di Ruberto, C., and Fodde, G. (2013, January 9\u201313). Evaluation of Statistical Features for Medical Image Retrieval. Proceedings of the International Conference on Image Analysis and Processing\u2014ICIAP 2013, Naples, Italy.","DOI":"10.1007\/978-3-642-41181-6_56"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error measurement to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_43","first-page":"56","article-title":"Importance of statistical measures in digital image processing","volume":"2","author":"Kumar","year":"2012","journal-title":"Int. J. Emerg. Technol. Adv. Eng."},{"key":"ref_44","first-page":"454","article-title":"A statistical feature-based approach for operations recognition in drilling time series","volume":"5","author":"Esmael","year":"2015","journal-title":"Int. J. Comput. Inf. Syst. Ind. Manag. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5732","DOI":"10.1118\/1.4747526","article-title":"Ultrasound GLCM texture analysis of radiation-induced parotid-gland injury in head-and-neck cancer radiotherapy: An in vivo study of late toxicity","volume":"39","author":"Yang","year":"2012","journal-title":"Med. Phys."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lee, C., Zhang, A., Yu, B., and Park, S. (2017). Comparison study between RMS and edge detection image processing algorithms for a pulsed laser UWPI (Ultrasonic wave propagation imaging)-based NDT technique. Sensors, 17.","DOI":"10.3390\/s17061224"},{"key":"ref_47","first-page":"1","article-title":"The statistical quantized histogram texture features analysis for image retrieval based on median and laplacian filters in the dct domain","volume":"10","author":"Malik","year":"2013","journal-title":"Int. Arab J. Inf. Technol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1177\/0013164414548576","article-title":"Descriptive statistics for modern test score distributions: Skewness, kurtosis, discreteness, and ceiling effects","volume":"75","author":"Ho","year":"2015","journal-title":"Educ. Psychol. Meas."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"59","DOI":"10.5815\/ijigsp.2016.11.08","article-title":"An automatic segmentation of brain tumor from MRI scans through wavelet transformations","volume":"8","author":"Kalaiselvi","year":"2016","journal-title":"Int. J. Image Graph. Signal Process."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1007\/s10044-017-0597-8","article-title":"Brain tumor classification from multi-modality MRI using wavelets and machine learning","volume":"20","author":"Usman","year":"2017","journal-title":"Pattern Anal. Appl."},{"key":"ref_51","unstructured":"O\u2019Hara, S., and Draper, B.A. (2011). Introduction to the bag of features paradigm for image classification and retrieval. arXiv."},{"key":"ref_52","unstructured":"Rueda, A., Arevalo, J., Cruz, A., Romero, E., and Gonz\u00e1lez, F.A. (2012, January 3\u20136). Bag of features for automatic classification of Alzheimer\u2019s disease in magnetic resonance images. Proceedings of the CIARP 2012 17th Iberoamerican Congress on Pattern Recognition, Buenos Aires, Argentina."},{"key":"ref_53","unstructured":"Le, X., and Gonzalez, R. (2009, January 16\u201318). Pattern-based corner detection algorithm. Proceedings of the 2009 6th International Symposium on Image and Signal Processing and Analysis, Salzburg, Austria."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","article-title":"Speeded-up robust features (SURF)","volume":"110","author":"Bay","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1002\/wics.1270","article-title":"Semi-supervised clustering methods","volume":"5","author":"Bair","year":"2013","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_56","unstructured":"Lindholm, A., Wahlstr\u00f6m, N., Lindsten, F., and Sch\u00f6n, T.B. (2020, December 15). Supervised Machine Learning. Lecture Notes for the Statistical Machine Learning Course. Available online: https:\/\/mwns.co\/blog\/wp-content\/uploads\/2020\/01\/Supervised-Machine-Learning.pdf."},{"key":"ref_57","unstructured":"Ghojogh, B., and Crowley, M. (2019). Linear and quadratic discriminant analysis: Tutorial. arXiv."},{"key":"ref_58","unstructured":"Evgeniou, T., and Pontil, M. (1999). Support vector machines: Theory and applications. Machine Learning and Its Applications, Proceedings of the ACAI 1999 Advanced Course on Artificial Intelligence, Springer."},{"key":"ref_59","unstructured":"Jakkula, V. (2006). Tutorial on Support Vector Machine (SVM), School of EECS, Washington State University."},{"key":"ref_60","unstructured":"Zeidat, N., Eick, C.F., and Zhao, Z. (2005). Supervised Clustering: Algorithms and Applications, University of Houston."},{"key":"ref_61","unstructured":"Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Van Esesn, B.C., Awwal, A.A.S., and Asari, V.K. (2018). The history began from alexnet: A comprehensive survey on deep learning approaches. arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"34","DOI":"10.12746\/swrccc.v5i19.391","article-title":"The receiver operating characteristic (ROC) curve","volume":"5","author":"Yang","year":"2017","journal-title":"Southwest Respir. Crit. Care Chron."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/778\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:14:45Z","timestamp":1760159685000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/778"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,24]]},"references-count":63,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21030778"],"URL":"https:\/\/doi.org\/10.3390\/s21030778","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,24]]}}}