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Cascade Network Model to Detect Cognitive Impairment using Clock Drawing Test

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Title Cascade Network Model to Detect Cognitive Impairment using Clock Drawing Test
 
Creator Talasila, Sri Lakshmi
R, Vijaya Kumari
 
Subject Automated CDT screening
Convolution neural network
Deep learning
Dementia
Feature fusion
 
Description 1276-1284
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%.
 
Date 2022-12-07T11:51:05Z
2022-12-07T11:51:05Z
2022-12
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61005
https://doi.org/10.56042/jsir.v81i12.69309
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.81(12) [December 2022]