The Alzheimer disease (AD) is a neurologic brain condition, which affects the cells in the brain and eventually renders a patient incapable of performing routine daily tasks. Due to the outstanding spatial clarity, high access, and strong contrast, MRI has been utilized in analyses pertaining to AD. This work develops an AD classification model using MRI images. Here, preprocessing is done by the Gabor filter. Subsequently, the Improved U-net segmentation model is employed for image segmentation. The features extracted comprises of modified LGXP features, LTP features, and LBP features as well. Finally, the Deep ensemble classifier (DEC) model is proposed for AD classification which combines classifiers such as RNN, DBN, and Deep Maxout Network (DMN). For enhancing the efficiency for classification of AD, the optimal weight of DMN is adjusted using the Self Customized BWO (SC-BWO) model. The outputs from DEC are averaged and the final result is obtained. Finally, the analysis of dice, Jaccard scores is performed to show the betterment of the SC-BWO scheme.
LianCLiuMZhangJShenD. Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI. IEEE Transactions on Pattern Analysis and Machine Intelligence.2018 Dec 21; 42(4): 880-93.
2.
MuruganSVenkatesanCSumithraMGGaoXZElakkiyaBAkilaMManoharanS. DEMNET: A deep learning model for early diagnosis of Alzheimer diseases and dementia from MR images. Ieee Access.2021 Jun 18; 9: 90319-29.
3.
HanRChenCPLiuZ. A novel convolutional variation of broad learning system for Alzheimer’s disease diagnosis by using MRI images. IEEE Access.2020 Nov 24; 8: 214646-57.
4.
BasherAKimBCLeeKHJungHY. Volumetric feature-based Alzheimer’s disease diagnosis from sMRI data using a convolutional neural network and a deep neural network. IEEE Access.2021 Feb 16; 9: 29870-82.
5.
ChoiJYLeeB. Combining of multiple deep networks via ensemble generalization loss, based on MRI images, for Alzheimer’s disease classification. IEEE Signal Processing Letters.2020 Jan 6; 27: 206-10.
6.
Ashtari-MajlanMSeifiADehshibiMM. A multi-stream convolutional neural network for classification of progressive MCI in Alzheimer’s disease using structural MRI images. IEEE Journal of Biomedical and Health Informatics.2022 Mar 3; 26(8): 3918-26.
7.
KujurARazaZKhanAAWechtaisongC. Data Complexity Based Evaluation of the Model Dependence of Brain MRI Images for Classification of Brain Tumor and Alzheimer’s Disease. IEEE Access.2022 Oct 21; 10: 112117-33.
8.
FaisalFUKwonGR. Automated detection of Alzheimer’s disease and mild cognitive impairment using whole brain MRI. IEEE Access.2022 Jun 3; 10: 65055-66.
9.
LuciaBEricWJoanaBP. Cerebrospinal fluid progranulin is associated with increased cortical thickness in early stages of Alzheimer’s disease, Neurobiology of Aging, In press, corrected proof, Available online 20 December 2019.
10.
ParkYHHodgesARisacherSLLinKJangJWAhnSKimSLovestoneSSimmonsAWeinerMWSaykinAJ. Dysregulated Fc gamma receptor-mediated phagocytosis pathway in Alzheimer’s disease: Network-based gene expression analysis. Neurobiology of Aging.2020 Apr 1; 88: 24-32.
11.
TadayonEPascual-LeoneAPressDSantarnecchiE, Alzheimer’s Disease Neuroimaging Initiative. Choroid plexus volume is associated with levels of CSF proteins: Relevance for Alzheimer’s and Parkinson’s disease. Neurobiology of Aging.2020 May 1; 89: 108-17.
12.
McKenzieCBucksRSWeinbornMBourgeatPSalvadoOGavettBE, Alzheimer’s Disease Neuroimaging Initiative. Cognitive reserve predicts future executive function decline in older adults with Alzheimer’s disease pathology but not age-associated pathology. Neurobiology of Aging.2020 Apr 1; 88: 119-27.
Martí-JuanGSanroma-GuellGPiellaG. A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer’s disease. Computer Methods and Programs in Biomedicine.2020 Jun 1; 189: 105348.
15.
LiuLZhaoSChenHWangA. A new machine learning method for identifying Alzheimer’s disease. Simulation Modelling Practice and Theory.2020 Feb 1; 99: 102023.
16.
Castillo-BarnesDSuLRamírezJSalas-GonzalezDMartinez-MurciaFJIllanIASegoviaFOrtizACruchagaCFarlowMRXiongC. Autosomal dominantly inherited alzheimer disease: Analysis of genetic subgroups by machine learning. Information Fusion.2020 Jun 1; 58: 153-67.
17.
LudwigNFehlmannTKernFGogolMMaetzlerWDeutscherSGurlitSSchulteCVon ThalerAKDeuschleCMetzgerF. Machine learning to detect Alzheimer’s disease from circulating non-coding RNAs. Genomics, Proteomics & Bioinformatics.2019 Aug 1; 17(4): 430-40.
18.
AllnuttMAJohnsonKBennettDAConnorSMTroncosoJCPletnikovaOAlbertMSResnickSMScholzSWDe JagerPLJacobsonS. Human herpesvirus 6 detection in Alzheimer’s disease cases and controls across multiple cohorts. Neuron.2020 Mar 18; 105(6): 1027-35.
19.
Amor-GutiérrezOCosta-RamaEArce-VarasNMartínez-RodríguezCNovelliAFernández-SánchezMTCosta-GarcíaA. Competitive electrochemical immunosensor for the detection of unfolded p53 protein in blood as biomarker for Alzheimer’s disease. Analytica Chimica Acta.2020 Jan 6; 1093: 28-34.
20.
GeCQuQGuIYJakolaAS. Multi-stream multi-scale deep convolutional networks for Alzheimer’s disease detection using MR images. Neurocomputing.2019 Jul 20; 350: 60-9.
21.
LiTRWangXNShengCLiYXLiFZSunYHanY. Extracellular vesicles as an emerging tool for the early detection of Alzheimer’s disease. Mechanisms of Ageing and Development.2019 Dec 1; 184: 111175.
22.
ShakaramiATarrahHMahdavi-HormatA. A CAD system for diagnosing Alzheimer’s disease using 2D slices and an improved AlexNet-SVM method. Optik.2020 Jun 1; 212: 164237.
23.
SunZQiaoYLelieveldtBPStaringM, Alzheimer’s Disease NeuroImaging Initiative. Integrating spatial-anatomical regularization and structure sparsity into SVM: Improving interpretation of Alzheimer’s disease classification. NeuroImage.2018 Sep 1; 178: 445-60.
24.
ZengNQiuHWangZLiuWZhangHLiY. A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease. Neurocomputing.2018 Dec 3; 320: 195-202.
25.
BasaiaSAgostaFWagnerLCanuEMagnaniGSantangeloRFilippiM, 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.2019 Jan 1; 21: 101645.
26.
VaithinathanKParthibanL, Alzheimer’s Disease Neuroimaging Initiative. A novel texture extraction technique with T1 weighted MRI for the classification of Alzheimer’s disease. Journal of Neuroscience Methods.2019 Apr 15; 318: 84-99.
27.
Samper-GonzálezJBurgosNBottaniSFontanellaSLuPMarcouxARoutierAGuillonJBacciMWenJBertrandA. Reproducible evaluation of classification methods in Alzheimer’s disease: Framework and application to MRI and PET data. NeuroImage.2018 Dec 1; 183: 504-21.
28.
MoscosoASilva-RodríguezJAldreyJMCortésJFernández-FerreiroAGómez-LadoNRuibalÁAguiarP, Alzheimer’s Disease Neuroimaging Initiative. Prediction of Alzheimer’s disease dementia with MRI beyond the short-term: Implications for the design of predictive models. NeuroImage: Clinical.2019 Jan 1; 23: 101837.
29.
MattssonNInselPSDonohueMJögiJOssenkoppeleROlssonTSchöllMSmithRHanssonO. Predicting diagnosis and cognition with 18F-AV-1451 tau PET and structural MRI in Alzheimer’s disease. Alzheimer’s & Dementia.2019 Apr 1; 15(4): 570-80.
30.
ShaikhTAAliR. Automated atrophy assessment for Alzheimer’s disease diagnosis from brain MRI images. Magnetic Resonance Imaging.2019 Oct 1; 62: 167-73.
31.
LahmiriSShmuelA. Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer’s disease. Biomedical Signal Processing and Control.2019 Jul 1; 52: 414-9.
32.
CasanovaRBarnardRTGaussoinSASaldanaSHaydenKMMansonJEWallaceRBRappSRResnickSMEspelandMAChenJC. Using high-dimensional machine learning methods to estimate an anatomical risk factor for Alzheimer’s disease across imaging databases. Neuroimage.2018 Dec 1; 183: 401-11.
33.
FanZZhangSMeiJLiuM. Recognition of woven fabric based on image processing and gabor filters. In 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE. 2017 Jul 31. pp. 996-1000.
34.
YangKFengXZhouJ. Fabric Defect Detection Based on the Optimal Gabor Filters. Journal of Minnan Normal University. 2016.
35.
ShanmugavadivuPBalasubramanianKSomasundaramK. Modified histogram equalization for image contrast enhancement using particle swarm optimization. Int. J. Comput. Sci. Eng. IT.2011 Dec; 1(5): 13-27.
36.
ZhangYTangZYangR. Data anomaly detection for structural health monitoring by multi-view representation based on local binary patterns. Measurement.2022 Oct 1; 202: 111804.
37.
XiaZYuanCSunXSunDLvR. Combining wavelet transform and LBP related features for fingerprint liveness detection. IAENG International Journal of Computer Science.2016 Apr; 43(3): 290-8.
38.
MondalSSadhuAKDuttaPK. Adaptive local ternary pattern on parameter optimized-faster region convolutional neural network for pulmonary emphysema diagnosis. IEEE Access.2021 Aug 16; 9: 114135-52.
39.
DongYWenRLiZZhangKZhangL. Clu-RNN: A new RNN based approach to diabetic blood glucose prediction. In 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology (ICBCB). IEEE. 2019 Mar 21. pp. 50-55.
40.
LuYShiYJiaGYangJ. A new method for semantic consistency verification of aviation radiotelephony communication based on LSTM-RNN. In 2016 IEEE International Conference on Digital Signal Processing (DSP). IEEE. 2016 Oct 16. pp. 422-426.
41.
KaoL-JChiuCC. Application of integrated recurrent neural network with multivariate adaptive regression splines on SPC-EPC process. Journal of Manufacturing Systems. 2020; 57: 109-118.
42.
GaoYZhangBWangSMaA. DBN based cloud service response time prediction method. In 2019 21st International Conference on Advanced Communication Technology (ICACT). IEEE. 2019 Feb 17. pp. 42-46.
43.
MaLYangYWangH. DBN based automatic modulation recognition for ultra-low SNR RFID signals. In 2016 35th Chinese Control Conference (CCC). IEEE. 2016 Jul 27. pp. 7054-7057.
44.
DengQLuPZhaoSYuanN. U-Net: A deep-learning method for improving summer precipitation forecasts in China. Atmospheric and Oceanic Science Letters.2023 Jul 1; 16(4): 100322.
45.
CaiMShiYLiuJ. Deep maxout neural networks for speech recognition. In 2013 IEEE Workshop on Automatic Speech Recognition and Understanding. IEEE. 2013 Dec 8. pp. 291-296.
NawazAAnwarSMLiaqatRIqbalJBagciUMajidM. Deep convolutional neural network based classification of Alzheimer’s disease using MRI data. In 2020 IEEE 23rd International Multitopic Conference (INMIC). IEEE. 2020 Nov 5. pp. 1-6.
49.
KhagiBKwonGR. 3D CNN based Alzheimer’s diseases classification using segmented Grey matter extracted from whole-brain MRI. JOIV: International Journal on Informatics Visualization.2021 Jun 30; 5(2): 200-5.
50.
RajasreeRSBrintha RajakumariS. Ensemble-of-classifiers-based approach for early Alzheimer’s Disease detection. Multimedia Tools and Applications.2023 Jul 12: 1-29.
51.
RSRRajakumariSB. Intelligence model for Alzheimer’s disease detection with optimal trained deep hybrid model. In Web Intelligence. IOS Press. No. Preprint, pp. 1-20.
52.
ShuklaATiwariRTiwariS. Structural biomarker-based Alzheimer’s disease detection via ensemble learning techniques. International Journal of Imaging Systems and Technology. 2023.
53.
MujahidMRehmanAAlamTAlamriFSFatiSMSabaT. An efficient ensemble approach for Alzheimer’s disease detection using an adaptive synthetic technique and deep learning. Diagnostics.2023 Jul 26; 13(15): 2489.
