INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.12 NO.05 M AY 2022
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.12 NO.05 M AY 2022
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International Journal of Innovative Technology & Creative Engineering Vol.12 No.05 May 2022
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.12 NO.05 M AY 2022
Dear Researcher, Greetings! Articles in this issue discusses about EARLY SCREENING OF ALZHEIMER DISEASE UTILIZING MACHINE LEARNING APPROACH - AN OVERVIEW.
We look forward many more new technologies in the next month.
Thanks, Editorial Team IJITCE
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Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbara Ph.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh,Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Technology Architect, Healthcare and Insurance Industry, Chicago, USA Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. Selvanathan Arumugam Ph.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA. Dr. S.Prasath Ph.D Assistant Professor, Department of Computer Science, VET Institute of Arts & Science (Co-Edu) College, Erode, Tamil Nadu, India Dr. P.Periyasamy, M.C.A.,M.Phil.,Ph.D. Associate Professor, Department of Computer Science and Applications, SRM Trichy Arts and Science College, SRM Nagar, Trichy - Chennai Highway, Near Samayapuram, Trichy - 621 105, Mr. V N Prem Anand Secretary, Cyber Society of India
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Review Board Members Dr. Rajaram Venkataraman Chief Executive Officer, Vel Tech TBI || Convener, FICCI TN State Technology Panel || Founder, Navya Insights || President, SPIN Chennai Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. MutamedTurkiNayefKhatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), TulKarm, PALESTINE. Dr.P.UmaMaheswari Prof &Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore. Dr. T. Christopher, Ph.D., Assistant Professor &Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India. Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India. Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 JavadRobati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran VineshSukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE Professor, JSPM's Rajarshi Shahu College of Engineering, MCA Dept., Pune, India. Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.12 NO.05 M AY 2022 Dr. doc. Ing. RostislavChoteborský, Ph.D. Katedramateriálu a strojírenskétechnologieTechnickáfakulta,Ceskázemedelskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21
Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg.,HamptonUniversity,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India). Dr. Ing. RostislavChotěborský,ph.d, Katedramateriálu a strojírenskétechnologie, Technickáfakulta,Českázemědělskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21
Dr. AmalaVijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE
Naik Nitin AshokraoB.sc,M.Sc Lecturer in YeshwantMahavidyalayaNanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed. Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-BangloreWesternly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech& PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,MechanicalEngineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ. Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY SeraphinChallyAbou Professor,Mechanical& Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 OrdeanCourt,Duluth, Minnesota 55812-3042 Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America
Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.12 NO.05 M AY 2022 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials CSIRO Process Science & Engineering Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688
Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar)01332-000, São Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road - Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India. Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India. Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India PremaSelvarajBsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),UniversitiSainsMalaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami Institute of Management & Technology, Hissar, Haryana, India
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.12 NO.05 M AY 2022 RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India Dr. S. B. Warkad Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India Dr. Saurabh Pal Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India
Manimala Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579 Dr. Qazi S. M. Zia-ul-Haque Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D. Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215 Dr. SeraphinChallyAbou Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 558123042 Dr. K. Kousalya Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052 Dr. (Mrs.) R. Uma Rani Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India. MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol"Noshirvani" University of Technology, Iran. Dr. Kulasekharan, N, Ph.D Technical Lead - CFD,GE Appliances and Lighting, GE India,John F Welch Technology Center,Plot # 122, EPIP, Phase 2,Whitefield Road,Bangalore – 560066, India. Dr. Manjeet Bansal Dean (Post Graduate),Department of Civil Engineering,Punjab Technical University,GianiZail Singh Campus,Bathinda -151001 (Punjab),INDIA Dr. Oliver Jukić Vice Dean for education,Virovitica College,MatijeGupca 78,33000 Virovitica, Croatia Dr. Lori A. Wolff, Ph.D., J.D. Professor of Leadership and Counselor Education,The University of Mississippi,Department of Leadership and Counselor Education, 139 Guyton University, MS 38677
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Contents EARLY SCREENING OF ALZHEIMER DISEASE UTILIZING MACHINE LEARNING APPROACH - AN OVERVIEW
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EARLY SCREENING OF ALZHEIMER DISEASE UTILIZING MACHINE LEARNING APPROACH AN OVERVIEW Dr. R. Hemalatha Head & Associate Professor, PG & Research Department of Computer Science, Tiruppur Kumaran College for Women, Bharathiar University, India {drhemalathacs@tkcfw.ac.in}
L. Subathra Devi Ph.D Research Scholar, PG & Research Department of Computer Science, Tiruppur Kumaran College for Women, Bharathiar University, India {subathradevics@tkcfw.ac.in}
Abstract— The application of Machine Learning within the field of diagnosis is increasing gradually. This can be contributed primarily to the development within the classification and recognition systems utilized in disease diagnosis which is in a position to supply data that aids doctors in early detection of fatal diseases and therefore, increase the survival rate of patients significantly, In this paper the diagnosis of Alzheimer’s disease (AD) or mild cognitive impairment (MCI) has attracted the attention of researchers during this field due to the rise within the occurrence of the diseases and therefore the need for early diagnosis. Unfortunately, the character of the high dimension of neural data and few available samples led to the creation of a particular computer diagnostic system. Machine learning techniques, especially deep learning, have been considered as a useful tool in this field. Analyzing magnetic resonance imaging (MRI) is a common practice for Alzheimer’s disease diagnosis in clinical research. Detection of Alzheimer’s disease is found by the similarity in Alzheimer’s disease MRI data and standard healthy MRI data of older people. Recently, advanced deep learning techniques have successfully demonstrated human-level performance in numerous fields including medical image analysis. We propose a deep convolutional neural network for Alzheimer’s disease diagnosis using brainMRI data analysis. While most of the prevailing approaches perform binary classification, our model can identify different stages of Alzheimer’s disease and obtains superior performance for early-stage diagnosis. The objective of this research study is to introduce
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a computer-aided diagnosis system for Alzheimer's disease detection using machine learning techniques. Keywords: Machine Learning, Alzheimer’s disease (AD), Magnetic Resonance Imaging (MRI), Neural Data, Convolutional Neural Network (CNN). I.INTRODUCTION Alzheimer’s disease (AD) is the most prevailing sort of dementia. It destroys brain cells causing people to lose their memory, mental functions and skill to continue daily activities. Initially, Alzheimer’s disease affects a part of the brain that controls language and memory. As a result, AD patients suffer from amnesia, confusion and difficulty in speaking, reading or writing. They often forget about their life and may not recognize their family members. Pathologically it is caused because of intracellular neurofibrillary tangles and extracellular amyloid protein and results in the deposition of plaques which obstruct the communication between the nerve cells resulting in this neurodegenerative disease. Brain Magnetic Resonance Imaging (MRI) has enabled noninvasive investigation of AD-related changes in the brain. Early detection is critical for effective management of Alzheimer’s disease (AD) and screening for Mild Cognitive Impairment (MCI) is common practice [1]. Among several techniques that are applied to assessing structural brain changes on Magnetic Resonance Imaging (MRI), Convolutional Neural Network (CNN) has gained popularity in automated feature learning with the utilization of a spread of multilayer perceptron.
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Machine learning is being widely used in various medical fields. Advances in medical technologies have given access to better data for identifying symptoms of various diseases in early stages. Alzheimer's disease is a chronic condition that leads to degeneration of brain cells leading to memory enervation. The aim of this paper is making use of machine learning algorithms to process this data obtained by neuroimaging technologies for detection of Alzheimer's in its primitive stage. II. STAGES OF ALZHEIMER'S Alzheimer’s has mostly severe physical and psychological effects on the person with Alzheimer’s and his family. Alzheimer’s normally starts with a slow progression and gets worse increasingly as time progresses. At the beginning of this disease, the first symptom that appears is memory loss. In the advanced stages, Alzheimer’s symptoms become more serious [2]. Alzheimer’s disease typically progresses slowly in three general stages: early, middle and late. A. Early-stage Alzheimer's (mild)
In the early stage of Alzheimer's, a person may function independently. He or she may still drive, work and be part of social activities. Despite this, the person may feel as if he or she is having memory lapses, such as forgetting familiar words or the location of everyday objects. Symptoms may not be widely apparent at this stage, but family and close friends may take notice and a doctor would be able to identify symptoms using certain diagnostic tools, Common difficulties include: Problems in remembering new facts Having difficulty performing tasks in social or work settings. Forgetting material that was just read. Losing or misplacing a valuable object. Experiencing increased trouble with planning or organizing. B. Middle-stage Alzheimer's (moderate)
Middle-stage Alzheimer's is typically the longest stage and can last for many years. As the disease progresses, the person with Alzheimer's will require a greater level of care. During the
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middle stage of Alzheimer’s, the dementia symptoms are more pronounced. The person may confuse words, get frustrated or angry, and act in unexpected ways, such as refusing to bathe. Damage to nerve cells in the brain can also make it difficult for the person to express thoughts and perform routine tasks without assistance. Symptoms, which vary from person to person, may include: Being forgetful of events or personal history. Feeling moody or withdrawn, especially in socially or mentally challenging situations. Being unable to recall information about themselves like their address or telephone number, and the high school or college they attended. Requiring help choosing proper clothing for the season or the occasion. Having trouble controlling their bladder and bowels. Experiencing changes in sleep patterns, such as sleeping during the day and becoming restless at night. Showing an increased tendency to wander and become lost. Demonstrating personality and behavioral changes, including suspiciousness and delusions or compulsive, repetitive behavior like hand-wringing or tissue shredding. C. Late-stage Alzheimer's (severe)
In the final stage of the disease, dementia symptoms are severe. Individuals lose the ability to respond to their environment, to carry on a conversation and eventually, to control movement. They may still say words or phrases, but communicating pain becomes difficult. As memory and cognitive skills continue to worsen, significant personality changes may take place and individuals need extensive care. Require around-the-clock assistance with daily personal care. Lose awareness of recent experiences as well as of their surroundings. Experience changes in physical abilities, including walking, sitting and eventually, swallowing. Become vulnerable to infections, especially pneumonia.
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The person living with Alzheimer’s may not be able to initiate engagement as much during the late stage, but he or she can still benefit from interaction in ways that are appropriate, like listening to relaxing music or receiving reassurance through gentle touch.
Fig 1: Stages in Alzheimer’s disease (AD)
III. SCREENING ALZHEIMER’S DISEASE BY USING MACHINE LEARNING The application of machine learning in a healthcare context is digital diagnosis. ML can detect patterns of certain diseases within patient electronic healthcare records and inform clinicians of any anomalies. Detection of Alzheimer’s disease (AD) is still not accurate until a patient reaches a moderate AD stage. The major challenge in this discussion is the high dimension with the small number of samples in the analysis of brain images. Therefore, machine learning and in their outline, deep learning, has achieved a lot of success. Machine learning methods can overcome these issues[4]. Recently, physicians are using brain MRI for Alzheimer’s disease diagnosis. A large number of research works focused on developing advanced machine learning models for AD diagnosis using MRI data [6], the Convolutional Neural Network (CNN) have demonstrated excellent performance in the field of medical imaging, i.e., segmentation, detection, and classification. So, recently researchers have started using CNN models for AD and other brain disease diagnosis. IV. MAGNETIC RESONANCE IMAGING (MRI) The primary role of MRI in the diagnosis of Alzheimer disease is the assessment of volume change in characteristic locations which can yield a diagnostic accuracy of up to 87%, Unfortunately, such volume loss is not apparent early in the course of the disease. A classic magnetic
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resonance imaging (MRI)-based automated AD diagnostic system has mainly two building blocks feature/biomarker extraction from the MRI data and classifier based on those features/biomarkers. there is a significant connection between the changes in brain tissues connectivity and behavior of AD patient, The changes causing AD due to the degeneration of brain cells are noticeable on image from different imaging modalities, e.g., structural and functional magnetic resonance imaging (sMRI, fMRI), Positron Emission Tomography (PET), Single Photon Emission Computed Tomography (SPECT) and Diffusion Tensor Imaging (DTI) scans. Several researchers have used these neuroimaging techniques for AD Diagnosis [3]. In neuroimaging, the main task is labeling anatomical structures in magnetic resonance imaging (MRI) brain scans with accuracy. For clinical decision-making, regional volume measurement is important, as well as accurate segmentation. Therefore, neuroimaging makes an optimistic prognosis more likely, and assessments by structural MRI (sMRI).
Fig 2: Image classification
V. CONVOLUTIONAL NEURAL NETWORK An automated image recognition method, the CNN has attracted widespread research attention with tremendous success in recent years. A convolution layer in the CNN model is typically composed of two segments: Feature Extraction and Feature Mapping. This unique network structure can effectively reduce the complexity of feedback neural networks, which characterizes the CNN model. With the CNN, each input image is passed through a series of convolution layers: filtering layers (kernels), pooling layers, and fully connected layers (FCs).[5] CNNs can directly accept images data as input, utilize spatial
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information embedded in adjacent pixels, and effectively reduce the number of model parameters by using local receptive fields, weights sharing, and subsampling. When a CNN model is trained with MRI slices, image features can be automatically retrieved, eliminating the need for manual selection of features for the learning process.
Fig 3: CNN Layer classification
These are the core layers of this class of networks. The convolutional layer obtains its output by applying the convolutional operation with different trainable kernels to the entire input, using a sliding window method to produce several feature maps containing different characteristics of the input marked in the feature extraction layer. The pooling layer segments the image into slices along with these above layers, then the CNN classification is described as: C(n)=(∑i=1kIn+i·f(n)) +b Where I is the input channel, f the filter, k the size of the filter, and b the bias. VI. PROPOSED MODEL The proposed model can classify different stages of Alzheimer’s disease and outperforms the of-the-shelf deep learning models. The primary contributions are threefold: 1. It proposes a deep Convolutional Neural Network that can identify Alzheimer’s Disease and classify the current disease stage. 2. The proposed network learns from a small dataset and still demonstrates superior performance for AD diagnosis. 3. It presents an efficient approach to training a deep learning model with an imbalanced dataset. The input MRI is an image classifier which identifies the level of stages, the proposed
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model is a 2D architecture devising an approach to convert the input data to 2D images. For each MRI data, the image slices will be then created from three physical planes of imaging: axial or horizontal plane, coronal or frontal plane and sagittal or median plane. VII. CONCLUSION The efficient approach to AD diagnosis using brain MRI data analysis. While the majority of the existing research works focuses on binary classification, this model provides significant improvement for multi-class classification. The Proposed network can be very beneficial for earlystage AD diagnosis. Though the proposed model has been tested only on AD dataset it has been diagnosed with other sets of classifiers, as the proposed approach has strong potential to be used for applying CNN into other areas with a limited dataset. In future, we plan to evaluate the proposed model for different AD datasets and other brain disease diagnosis. REFERENCES [1] B. C. Riedel, M. Daianu, G. Ver Steeg et al., “Uncovering biologically coherent peripheral signatures of health and risk for Alzheimer’s disease in the aging brain,” Frontiers in Aging Neuroscience, vol. 10, p. 390, 2018. [2] Greene, S. J., and Killiany, R. J. Alzheimer’s Disease Neuroimaging Initiative (2012). Hippocampal subregions are differentially affected in the progression to Alzheimer’s disease. Anatom. Rec. 295, 132–140. doi: 10.1002/ar.21493 [3] Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). “ImageNet classification with deep convolutional neural networks,” in Proceedings of the International Conference on Neural Information Processing Systems (Cambridge, MA: MIT Press), 1097–1105. [4] Serra L, Cercignani M, Mastropasqua C, Torso M, Spanò B, Makovac E, ViolaV, Giulietti G, Marra C, Caltagirone C et al (2016) Longitudinal changes in functional brain connectivity predicts conversion to Alzheimer’s disease. 51(2):377–389 [5] T. H. Gorji and K. Naima, “A deep learning approach for diagnosis of mild cognitive impairment based on MRI images,” Brain Sciences, vol. 9, no. 9, p. 217, 2019. [6] McGeer, P.L. Brain imaging in Alzheimer’s disease. Br. Med. Bull. 1986, 42, 24–28. [Google Scholar] [CrossRef] [PubMed].
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