[Abstract] Automatic detection of Alzheimer’s disease is a very active area of research. This is due to its usefulness in starting the protocol to stop the inevitable progression of this neurodegenerative disease. This paper proposes a system for the detection of the disease by means of Deep Learning techniques in magnetic resonance imaging (MRI). As a solution, a model of neuronal networks (ANN) and two sets of reference data for training are proposed. Finally, the goodness of this system is verified within the domain of the application

Fernández-Blanco, Enrique

Munteanu, Cristian-Robert

Puente-Castro, Alejandro

Proceedings

English

Automatic detection of Alzheimer&rsquo;s disease is a very active area of research. This is due to its usefulness in starting the protocol to stop the inevitable progression of this neurodegenerative disease. This paper proposes a system for the detection of the disease by means of Deep Learning techniques in magnetic resonance imaging (MRI). As a solution, a model of neuronal networks (ANN) and two sets of reference data for training are proposed. Finally, the goodness of this system is verified within the domain of the application

Alejandro Puente-Castro

Multidisciplinary Digital Publishing Institute

System for Automatic Assessment of Alzheimer’s Disease Diagnosis Based on Deep Learning Techniques

Repositorio da Universidade da Coruña

proceedingsProceedingsSystem for Automatic Assessment of Alzheimer’sDisease Diagnosis Based on DeepLearning Techniques †Alejandro Puente-Castro * , Cristian Robert Munteanu and Enrique Fernandez-BlancoFaculty of Computer Science, CITIC, University of A Coruna, 15071 La Coruña, Spain* Correspondence: a.puentec@udc.es† Presented at the 2nd XoveTIC Conference, A Coruna, Spain, 5–6 September 2019.Published: 1 August 2019Abstract: Automatic detection of Alzheimer’s disease is a very active area of research. This is due toits usefulness in starting the protocol to stop the inevitable progression of this neurodegenerativedisease. This paper proposes a system for the detection of the disease by means of Deep Learningtechniques in magnetic resonance imaging (MRI). As a solution, a model of neuronal networks (ANN)and two sets of reference data for training are proposed. Finally, the goodness of this system isverified within the domain of the application.Keywords: Alzheimer; Deep learning; MRI; sagittal; ANN; transfer learning1. IntroductionThe progressive and constant aging of the population causes a higher incidence ofneurodegenerative diseases associated with age. Among these diseases, the Alzheimer’s has aprevalence of 5.05% in Europe as early as 2016 [1]. Early palliative diagnosis and treatment continueto be the best alternatives for improving the patient’s quality of life and their environment. To makethis diagnosis, various cognitive, psychological or clinical tests are used. Within the group of clinicaltests, one of the most widely used is the analysis of brain images obtained by magnetic resonanceimaging (MRI), as changes in brain morphology can be seen such as contraction of the hippocampusand cerebral cortex, or elongation of the ventricles [2].The objective of this work is the use of Deep Learning techniques to support an early diagnosis ofAlzheimer’s disease through the analysis of conventional sagittal MRI images from two reference setsof data.2. Materials and MethodsThis section describes the datasets and computer models proposed in this work, each of which isdescribed in its corresponding subsection.2.1. MaterialsThis work makes the MRI images dataset ADNI [3]. Both sets are collections of correctly labeledMRI images of 255 × 255 pixels, which are two of the most common in the literature.2.2. Proposed ModelThe proposed method uses the classical pipeline to solve a problem on any kind of signal, and inparticular in this case images. This pipeline has a pre-established set of stages, being: pre-processingProceedings 2019, 21, 28; doi:10.3390/proceedings2019021028 www.mdpi.com/journal/proceedingsProceedings 2019, 21, 28 2 of 3phase, feature extraction phase and a regression or classification phase based on the features extractedfrom the images.The proposed model is based on the use of a pre-trained model which, in this particular case isResNet [4]. The idea behind this is to take advance of features extration phase of the model while theclassification is droped or ajusted for a new problem. This squema known under the name of transferlearning has been used many times in the related literature. In the proposed model, the classifier hasbeen replaced by a Support Vector Machine (SVM) [5]. Thus, the speed of experimentation can beaccelerated by adapting to this problem, successful models in other different problems.3. ResultsIn this paper, the results for the ADNI dataset are presented. The results shown on Table 1 areaverage of 50 repretitions of a Hold-Out training strategy. The table showns the test result comparisonbetween a reference work and two developed developped models, one with ResNet as base and onewith MobileNet [6]. The dataset was splited in 80% for training and 20% for testing, while a 1% ofthe training data set was used for validation purposes. The advantages of the ResNet approach arenoticeable being the best one in precision and recall which are our main objective.Table 1. Best results for ADNI.Model Image Accuracy Precision Recall Specificity f1-Score AUCInception [7] PET hor. - 63.66% 64.67% 79.00% 64.00% 76.00%ResNet MRI sag. 81.46%±1.9% 82.48%±2.2% 93.09%±1.9% 55.19%±4.1% 87.44%±1.5% 74.14%±2.2%MobileNet MRI sag. 51.08%±19.1% 37.56%±34.7% 52.4%±49.7% 47.73%±40.7% 43.16%±40.7% 50.01%±0.1%4. DiscussionAs a main conclusion the identification of Alzheimer’s disease in sagittal MRI images from ADNIdataset is accessible using Deep Learning techniques. These results are comparable to those proposedby the horizontal cuts in the literature. Despite the high imbalance of both data sets and the smallOASIS set, the proposed model presents satisfactory results because of its simplicity.Based on the authors’ experience in the field of Alzheimer’s disease, the sagittal plane also showscharacteristic deformations of the disease. Traditionally, specialists use the horizontal plane. This opensup new ways for experimentation. New characteristics of Alzheimer’s disease can be found in them inother regions. These new features may make the diagnosis of Alzheimer’s a more precise task.Author Contributions: Conceptualization, A.P.-C.; methodology, A.P.-C.; experiment design, C.M.; software,A.P.-C. and E.F.-B.; validation, A.P.-C.; formal analysis, A.P.-C.; investigation, A.P.-C.; resources, A.P.-C.; datacuration, A.P.-C.; writing—original draft preparation, A.P.-C.; writing—review and editing, E.F.-B.; visualization,A.P.-C.; supervision, C.M. and E.F.-B.Funding: This research received no external funding.Acknowledgments: The authors would like to thank the support from NVidia corp., which granted the GPU usedin this work. They also acknowledge the support from the CESGA, where many of the preliminary tests were run.Conflicts of Interest: The authors declare no conflict of interest.AbbreviationsThe following abbreviations are used in this manuscript:ANN Artificial Neural NetworkMRI Magnetic Resonance ImagingAUC Area Under Curve ROCROC Receiver Operating CharacteristicProceedings 2019, 21, 28 3 of 3References1. Niu, H.; Álvarez-Álvarez, I.; Guillén-Grima, F.; Aguinaga-Ontoso, I. Prevalencia e incidencia de laenfermedad de Alzheimer en Europa: Metaanálisis. Neurología 2017, 32, 523–532.2. Sarraf, S.; Tofighi, G. DeepAD: Alzheimer’s disease classification via deep convolutional neural networksusing MRI and fMRI. BioRxiv 2016, p. 070441.3. ADNI|Alzheimer’s Disease Neuroimaging Initiative. Avaialble online: http://adni.loni.usc.edu (accessedon 27 June 2019).4. He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEEConference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 770–778.5. Joachims, T. Text categorization with support vector machines: Learning with many relevant features. InEuropean Conference on Machine Learning; Springer: Berlin/Heidelberg, Germany, 1998; pp. 137–142.6. Howard, A.G.; Zhu, M.; Chen, B.; Kalenichenko, D.; Wang, W.; Weyand, T.; Andreetto, M.; Adam,H. Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv 2017,arXiv:1704.04861 .7. Ding, Y.; Sohn, J.H.; Kawczynski, M.G.; Trivedi, H.; Harnish, R.; Jenkins, N.W.; Lituiev, D.; Copeland, T.P.;Aboian, M.S.; Mari Aparici, C.; et al. A Deep learning model to predict a diagnosis of alzheimer disease byusing 18F-FDG PET of the brain. Radiology 2018, 290, 456–464.c© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Crossref

https://ruc.udc.es/dspace/bitstream/handle/2183/23936/A.Puente-Casto_2019_System_for_Automatic_Assessment_of_Alzheimer%e2%80%99s.pdf?sequence=3&isAllowed=y

System for Automatic Assessment of Alzheimer’s Disease Diagnosis Based on Deep Learning Techniques

Abstract

Similar works

Full text

Available Versions

Multidisciplinary Digital Publishing Institute

Repositorio da Universidade da Coruña

Crossref