Journal of Scientific and Industrial Research

Open Access Open Access  Restricted Access Subscription Access

Cascade Network Model to Detect Cognitive Impairment Using Clock Drawing Test


Affiliations
1 Computer Science, Krishna University, Machilipatnam, 521 004, Andhra Pradesh, India
 

The Clock-Drawing Test (CDT) is commonly used to screen people for assessing cognitive impairment. Diagnoses are based on analyzing the specific features of clock drawing with pen and paper. The manual interpretations and understanding of the features are time-consuming, and test results highly depend on clinical experts' knowledge. Due to the impact of smart devices and advancements in deep learning algorithms, the necessity of a consistent and automatic screening system for cognitive impairment has amplified. This work proposed a simple, fast, low-cost, automated CDT screening technique. Initially, transferred deep convolution neural networks (ResNet152, EfficientNetB4, and DenseNet201) are used as feature extractors. The transfer learning technique makes it possible to experiment with existing models and build models much more quickly. Further, the extracted features are cascaded into a feature fusion layer to improve the quality of learning features, and the obtained feature vector become input for the classifier for classification. The performance of the model is experimentally evaluated and compared with the existing state-of-art models on a real dataset. Obtained results demonstrated that the Cascaded Network Model achieves high performance with an accuracy of 97.76%.

Keywords

Automated CDT Screening, Convolution Neural Network, Deep Learning, Dementia, Feature Fusion.
User
Notifications
Font Size

  • Alzheimer's Association, Alzheimer's Disease Facts and Figures, https://www.alz.org/ alzheimers-dementia/facts-figures, 2020 (September 20, 2021).

  • Alzheimer's & Related Disorders Society of India, https://www.alzint.org/member/alzheimers-related-disorders-society-ofindia-ardsi, 2021, (September 20, 2021).

  • DeTure M A & Dickson D W, The neuropathological diagnosis of Alzheimer’s disease, Mol Neurodegener, 14(1) (2019) 1–18.

  • Freedman M, Leach L, Kaplan E, Shulman K & Delis D C, Clock drawing: A neuropsychological analysis, Oxford University Press, USA (1994).

  • Janakiramaiah B, Kalyani G & Jayalakshmi A, Automatic alert generation in a surveillance systems for smart city environment using deep learning algorithm, Evol Intell, 14(2) (2021) 635–642.

  • Ryu S Y, Lee S B, Kim Y I & Lee K S, The utility of the clock drawing test for cognitive impairment screening, Alzheimers Dement, 2 (2006) Poster 2, Article 113.

  • Umegaki H, Suzuki Y, Yamada Y, Komiya H, Watanabe K, Nagae M &Kuzuya M, Association of the qualitative clock drawing test with progression to dementia in non-demented older adults, J Clin Med, 9(9) (2020) 2850.

  • Cacho J, Benito-León J, García-García R, Fernández-Calvo B, Vicente-Villardón J L & Mitchell A J, Does the combination of the MMSE and clock drawing test (mini-clock) improve the detection of mild Alzheimer's disease and mild cognitive impairment?, J Alzheimers Dis, 22(3) (2010) 889–896.

  • Mittal C, Gorthi S P & Rohatgi S, Early cognitive impairment: role of clock drawing test, Med J Armed Forces India, 66(1) (2010) 25–28.

  • Eknoyan D, Hurley R A & Taber K H, The clock drawing task: common errors and functional neuroanatomy, J Neuropsychiatry Clin Neurosci, 24(3) (2012) 260–265.

  • Piers R J, Devlin K N, Ning B, Liu Y, Wasserman B, Massaro J M & Libon D J, Age and graphomotor decision making assessed with the digital clock drawing test: the Framingham heart study, J Alzheimers Dis, 60(4) (2017) 1611–1620.

  • Cohen J, Penney D L, Davis R, Libon D J, Swenson R A, Ajilore O & Lamar M, Digital clock drawing: differentiating “thinking” versus “doing” in younger and older adults with depression, J Int Neuropsychol Soc, 20(9) (2014) 920–928.

  • Binaco R, Calzaretto N, Epifano J, McGuire S, Umer M, Emrani S, ... & Polikar R, Machine learning analysis of digital clock drawing test performance for differential classification of mild cognitive impairment subtypes versus Alzheimer’s disease, J Int Neuropsychol Soc, 26(7) (2020) 690–700.

  • Zheng X, Zhang W, Wang X, Li R, Liu M, Xu F, Li Y, Zheng J & Nie Z, Extended application of digital clock drawing test in the evaluation of Alzheimer's disease based on artificial intelligence and the neural basis, Curr Alzheimer Res, 18(14) (2021) 1127–1139.

  • https://anest.ufl.edu/2022/06/16/research-expands-use-of-clock-drawing-test-for-dementia-screening/

  • Li F, Tran L, Thung K H, Ji S, Shen D & Li J, A robust deep model for improved classification of AD/MCI patients, IEEE J Biomed Health Inform, 19(5) (2015) 1610–1616.

  • Amini S, Zhang L, Hao B, Gupta A, Song M, Karjadi C, & Paschalidis I C, An ai-assisted online tool for cognitive impairment detection using images from the clock drawing test, medRxiv (2021), https://doi.org/10.1101/2021.03.06.21253047.

  • Park I & Lee U, Automatic, qualitative scoring of the clock drawing test (CDT) based on u-net, CNN and mobile sensor data, Sensors, 21(15) (2021) 5239.

  • Youn Y C, Pyun J M, Ryu N, Baek M J, Jang J W, Park Y H, ... & Kim S Y, Use of the clock drawing test and the rey–osterrieth complex figure test-copy with convolutional neural networks to predict cognitive impairment, Alzheimer's Res Ther, 13(1) (2021) 1–7.

  • Heimann-Steinert A, Latendorf A, Prange A, Sonntag D & Müller-Werdan U, Digital pen technology for conducting cognitive assessments: a cross-over study with older adults, Psychol Res, 85(8) (2021) 3075–3083.

  • Sato K, Niimi Y, Mano T, Iwata A & Iwatsubo T, Automated evaluation of conventional clock-drawing test using deep neural network: Potential as a mass screening tool to detect individuals with cognitive decline, Front Neurol, 13 (2022) 896403–896403.

  • Pan D, Zeng A, Jia L, Huang Y, Frizzell T & Song X, Early detection of Alzheimer’s disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning, Front Neurosci, 14 (2020) 259.

  • Zheng L, Zhao Y, Wang S, Wang J & Tian Q, Good practice in CNN feature transfer, arXiv preprint (2016) arXiv: 1604.00133, https://doi.org/10.48550/arXiv.1604.00133.

  • Kawahara J & Hamarneh G, Multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained layers, in Machine Learning in Medical Imaging, edited by L Wang, E Adeli, Q Wang, Y Shi, H I Suk, MLMI 2016, Lecture Notes in Computer Science (Springer, Cham) vol 10019, (2016) 164–171 https://doi.org/10.1007/978-3-319-47157-0_20.

  • Pan S J & Yang Q, A survey on transfer learning, IEEE Trans Knowl Data Eng, 22(10) (2009) 1345–1359.

  • He K, Zhang X, Ren S & Sun J, Deep residual learning for image recognition, Proc IEEE Conf on Comput Vision Pattern Recognit, (2016) (pp 770–778).

  • Tan M & Le Q, Efficientnet: Rethinking model scaling for convolutional neural networks, in IEEE Int Conf Machine Learn, (California, USA) 9–15, June 2019, (pp 6105–6114) PMLR.

  • Huang G, Liu Z, Van Der Maaten L & Weinberger K Q, Densely connected convolutional networks, Proc IEEE Conf on Comput Vision Pattern Recognit, (2017) (pp 4700–4708).

  • Subramanian M, Narasimha Prasad L V, Janakiramaiah B, Mohan Babu A & Sathishkumar Ve, Hyperparameter optimization for transfer learning of VGG16 for disease identification in corn leaves using Bayesian optimization, Big Data, 10(3) (2022) 215–229, DOI: 10.1089/big.2021.0218

  • NHATS Public Use Data. (Insert Round or Rounds), sponsored by the National Institute on Aging (grant number NIA U01AG032947) through a cooperative agreement with the Johns Hopkins Bloomberg School of Public Health, Available at www.nhats.org.

  • Chen S, Stromer D, Alabdalrahim H A, Schwab S, Weih M & Maier A, Automatic dementia screening and scoring by applying deep learning on clock-drawing tests, Sci Rep, 10(1) (2020) 1–11.


Abstract Views: 51

PDF Views: 49




  • Cascade Network Model to Detect Cognitive Impairment Using Clock Drawing Test

Abstract Views: 51  |  PDF Views: 49

Authors

Sri Lakshmi Talasila
Computer Science, Krishna University, Machilipatnam, 521 004, Andhra Pradesh, India
Vijaya Kumari R
Computer Science, Krishna University, Machilipatnam, 521 004, Andhra Pradesh, India

Abstract


The Clock-Drawing Test (CDT) is commonly used to screen people for assessing cognitive impairment. Diagnoses are based on analyzing the specific features of clock drawing with pen and paper. The manual interpretations and understanding of the features are time-consuming, and test results highly depend on clinical experts' knowledge. Due to the impact of smart devices and advancements in deep learning algorithms, the necessity of a consistent and automatic screening system for cognitive impairment has amplified. This work proposed a simple, fast, low-cost, automated CDT screening technique. Initially, transferred deep convolution neural networks (ResNet152, EfficientNetB4, and DenseNet201) are used as feature extractors. The transfer learning technique makes it possible to experiment with existing models and build models much more quickly. Further, the extracted features are cascaded into a feature fusion layer to improve the quality of learning features, and the obtained feature vector become input for the classifier for classification. The performance of the model is experimentally evaluated and compared with the existing state-of-art models on a real dataset. Obtained results demonstrated that the Cascaded Network Model achieves high performance with an accuracy of 97.76%.

Keywords


Automated CDT Screening, Convolution Neural Network, Deep Learning, Dementia, Feature Fusion.

References