Alzheimer's disease (AD), which occurs as a result of the loss of cognitive functions in the brain, causes near-forgetfulness in the case and dementia in subsequent processes. Dataset consists of MR images containing four phases of AD. The dataset was re-enhanced separately with DeepDream, fuzzy color image enhancement, hypercolumn techniques. Visual Geometry Group-16 (VGG-16) deep learning model is used in the enhancing process and deep features are combined. Linear Regression is used for the selection of efficient features. The Support Vector Machine is preferred as a classifier. With the proposed approach, the classification achievement was obtained as 100% in Mild Dementia, 99.94% in Moderate Dementia, 100% in non-Dementia, 99.94% in Very Mild Dementia. The overall accuracy was 99.94%. The proposed approach increased the prediction success in detecting Alzheimer's stages by re-enhancing MR images. Thus, an efficient early diagnosis model was realized at an affordable cost for individuals likely to progress with dementia.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Weller J, Budson A (2018) Current understanding of Alzheimer’s disease diagnosis and treatment. F1000Research 7:F1000 Faculty Rev-1161. https://doi.org/https://doi.org/10.12688/f1000research.14506.1
Weller J, Budson A (2019) 2019 Alzheimer’s disease facts and figures. Alzheimer’s Dement 15:321–387. https://doi.org/10.1016/j.jalz.2019.01.010
Qiu S, Heydari MS, Miller MI et al (2019) P1–119: enhancing deep learning model performance for AD diagnosis using ROI-based selection. Alzheimer’s Dement 15:P280–P281. https://doi.org/10.1016/j.jalz.2019.06.674
Basaia S, Agosta F, Wagner L et al (2019) Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage Clin 21:101645. https://doi.org/10.1016/j.nicl.2018.101645
Liu M, Li F, Yan H et al (2020) A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease. Neuroimage 208:116459. https://doi.org/10.1016/j.neuroimage.2019.116459
Lu X, Wu H, Zeng Y (2019) Classification of Alzheimer’s disease in MobileNet. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/1345/4/042012
Shen T, Jiang J, Lu J et al (2019) Predicting alzheimer disease from mild cognitive impairment with a deep belief network based on 18F-FDG-PET images. Mol Imag 18:1–9. https://doi.org/10.1177/1536012119877285
Xiao Z, Ding Y, Lan T et al (2017) Brain MR image classification for alzheimer’s disease diagnosis based on multifeature fusion. Comput Math Methods Med 2017:1952373. https://doi.org/10.1155/2017/1952373
Jo T, Nho K, Saykin AJ (2019) Deep learning in alzheimer’s disease: diagnostic classification and prognostic prediction using neuroimaging data. Front Aging Neurosci 11:220. https://doi.org/10.3389/fnagi.2019.00220
Menger V, Scheepers F, Spruit M (2018) Comparing deep learning and classical machine learning approaches for predicting inpatient violence incidents from clinical text. Appl Sci 8:981. https://doi.org/10.3390/app8060981
Nguyen G, Dlugolinsky S, Bobák M et al (2019) Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey. Artif Intell Rev 52:77–124. https://doi.org/10.1007/s10462-018-09679-z
Sze V, Chen YH, Yang TJ, Emer JS (2017) Efficient processing of deep neural networks: a tutorial and survey. Proc IEEE 105:2295–2329. https://doi.org/10.1109/JPROC.2017.2761740
Dubey S (2020) Alzheimer’s Dataset four class of Images. In: Kaggle. https://www.kaggle.com/tourist55/alzheimers-dataset-4-class-of-images/data. Accessed 1 Mar 2020
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations
Toğaçar M, Ergen B (2018) Deep learning approach for classification of breast cancer. In: 2018 International conference on artificial ıntelligence and data processing (IDAP). pp 1–5
Guan Q, Wang Y, Ping B et al (2019) Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study. J Cancer 10:4876–4882. https://doi.org/10.7150/jca.28769
Cengil E, Çınar A (2016) A new approach for image classification: convolutional neural network. Eur J Tech EJT 6:96–103
Demir F, Şengür A, Bajaj V, Polat K (2019) Towards the classification of heart sounds based on convolutional deep neural network. Heal Inf Sci Syst 7:16. https://doi.org/10.1007/s13755-019-0078-0
Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88. https://doi.org/10.1016/j.media.2017.07.005
Başaran E, Cömert Z, Çelik Y (2020) Convolutional neural network approach for automatic tympanic membrane detection and classification. Biomed Signal Process Control 56:101734. https://doi.org/10.1016/j.bspc.2019.101734
Deniz E, Sengür A, Kadiroglu Z et al (2018) Transfer learning based histopathologic image classification for breast cancer detection. Heal Inf Sci Syst 6:18. https://doi.org/10.1007/s13755-018-0057-x
Lu S, Lu Z, Zhang Y-D (2018) Pathological brain detection based on AlexNet and transfer learning. J Comput Sci. https://doi.org/10.1016/j.jocs.2018.11.008
Gu J, Wang Z, Kuen J et al (2018) Recent advances in convolutional neural networks. Pattern Recognit 77:354–377. https://doi.org/10.1016/j.patcog.2017.10.013
Sertkaya ME, Ergen B, Togacar M (2019) Diagnosis of eye retinal diseases based on convolutional neural networks using optical coherence ımages. In: 2019 23rd ınternational conference electronics, pp 1–5
Celik Y, Talo M, Yildirim O et al (2020) Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images. Pattern Recognit Lett 133:232–239. https://doi.org/10.1016/j.patrec.2020.03.011
Ma Y, Zhang Q, Li D, Tian Y (2019) Linex support vector machine for large-scale classification. IEEE Access 7:70319–70331. https://doi.org/10.1109/access.2019.2919185
Bisgin H, Bera T, Ding H et al (2018) Comparing SVM and ANN based machine learning methods for species identification of food contaminating beetles. Sci Rep 8:1–12. https://doi.org/10.1038/s41598-018-24926-7
Toğaçar M, Ergen B, Cömert Z (2020) Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models. Measurement 153:107459. https://doi.org/10.1016/j.measurement.2019.107459
Awad M, Khanna R (2015) Support vector machines for classification BT—efficient learning machines: theories, concepts, and applications for engineers and system designers. In: Awad M, Khanna R (eds). Apress, Berkeley, CA, pp 39–66
Doǧan Ü, Glasmachers T, Igel C (2016) A unified view on multi-class support vector classification. J Mach Learn Res 17:1–32
(2020) DeepDream | TensorFlow Core. In: TensofFlow. https://www.tensorflow.org/tutorials/generative/deepdream. Accessed 30 Aug 2020
Suzuki K, Roseboom W, Schwartzman DJ, Seth AK (2017) A deep-dream virtual reality platform for studying altered perceptual phenomenology. Sci Rep 7:1–11. https://doi.org/10.1038/s41598-017-16316-2
Bardak T, Bardak S (2017) Prediction of wood density by using red-green-blue (RGB) color and fuzzy logic techniques. J Polytech 20:979–984. https://doi.org/10.2339/politeknik.369132
Arnal J, Súcar L (2020) Hybrid filter based on fuzzy techniques for mixed noise reduction in color images. Appl Sci. https://doi.org/10.3390/app10010243
Yun HJ, Wu ZY, Wang GJ et al (2016) A novel enhancement algorithm combined with improved fuzzy set theory for low illumination images. Math Probl Eng. https://doi.org/10.1155/2016/8598917
Patrascu V (2019) Fuzzy color ımage enhancement algorithm. In: Github. https://github.com/WaseemKn/FuzzyColorImageEnhancement-FuzzyLogicCourse-ITE5thYear. Accessed 1 Mar 2020
Pilly PK, Stepp ND, Liapis Y et al (2019) Hypercolumn sparsification for low-power convolutional neural networks. J Emerg Technol Comput Syst 15:20. https://doi.org/10.1145/3304104
Hariharan B, Arbeláez P, Girshick R, Malik J (2015) Hypercolumns for object segmentation and fine-grained localization. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. https://doi.org/10.1109/cvpr.2015.7298642
Sreeraman R (2018) Hypercolumn test VGG16. In: Kaggle. https://www.kaggle.com/rupeshs/hypercolumn-test-vgg16. Accessed 1 Sep 2020
Hariharan B, Arbeláez P, Girshick R, Malik J (2017) Object instance segmentation and fine-grained localization using hypercolumns. IEEE Trans Pattern Anal Mach Intell 39:627–639. https://doi.org/10.1109/tpami.2016.2578328
Gueorguieva N, Valova I, Klusek D (2020) Solving large scale classification problems with stochastic based optimization. Procedia Comput Sci 168:26–33. https://doi.org/10.1016/j.procs.2020.02.247
Rzecki K, Sośnicki T, Baran M, Niedźwiecki M, Król M, Łojewski T, Acharya UR, Yildirim Ö, Pławiak P (2018) Application of computational intelligence methods for the automated identification of paper-ink samples based on LIBS. Sensors 18:3670. https://doi.org/10.3390/s18113670
Zhuo L, Zhang B, Chen C et al (2019) Calibrated stochastic gradient descent for convolutional neural networks. Proc AAAI Conf Artif Intell 33:9348–9355. https://doi.org/10.1609/aaai.v33i01.33019348
Schneider A, Hommel G, Blettner M (2010) Linear regression analysis: part 14 of a series on evaluation of scientific publications. Dtsch Arztebl Int 107:776–782. https://doi.org/10.3238/arztebl.2010.0776
Hoffman JIE (2019) Chapter 27—linear regression. In: Hoffman JIE (ed) Basic biostatistics for medical and biomedical practitioners, 2nd edn. Academic Press, Cambridge, pp 445–489
(2020) Feature selection: linear regression. In: Scikit - Learn. https://scikit-learn.org/stable/modules/feature_selection.html. Accessed 3 Mar 2020
Abdullah AO, Ali MA, Karabatak M, Sengur A (2018) A comparative analysis of common YouTube comment spam filtering techniques. In: 2018 6th ınternational symposium on digital forensic and security (ISDFS), pp 1–5
Günay M, Köseoğlu M, Yıldırım Ö (2020) Classification of hand-drawn basic circuit components using convolutional neural networks. In: 2020 ınternational congress on human-computer ınteraction, optimization and robotic applications (HORA), pp 1–5
Parvandeh S, Yeh H-W, Paulus MP, McKinney BA (2020) Consensus features nested cross-validation. bioRxiv 2019.12.31.891895. https://doi.org/https://doi.org/10.1101/2019.12.31.891895
Yadav S, Shukla S (2016) Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification. In: 2016 IEEE 6th ınternational conference on advanced computing (IACC), pp 78–83
(2020) Alzheimer’s Stages - Early, Middle, Late Dementia Symptoms | alz.org. In: Alzheimer’s Assoc. https://www.alz.org/alzheimers-dementia/stages. Accessed 4 Mar 2020
There is no funding source for this article.
Conflict of interest
The authors declare that there is no conflict of interest related to this paper.
This article does not contain any data, or other information from studies or experimentation, with the involvement of human or animal subjects.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Toğaçar, M., Cömert, Z. & Ergen, B. Enhancing of dataset using DeepDream, fuzzy color image enhancement and hypercolumn techniques to detection of the Alzheimer's disease stages by deep learning model. Neural Comput & Applic (2021). https://doi.org/10.1007/s00521-021-05758-5
- Alzheimer's disease
- DeepDream learning and hypercolumn technique
- Fuzzy color image enhancement