Classification Model of Clock Drawing Test Based on Contrastive Learning Using Multi-Channel Features With Channel-Spatial Attention Article Swipe
ChangSu Kang
,
Bohyun Wang
,
Joon S. Lim
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3436102
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3436102
The Clock Drawing Test (CDT) is a professional examination that can detect cognitive impairments, such as Parkinson’s and Alzheimer’s diseases, based on scoring criteria. The pooling layers of a convolutional neural network (CNN) compress features by reducing dimensionality, which tends to focus on a single dominant element. This can be detrimental to compressing information in CDT images, where all elements are significant features. Therefore, in this study, we developed a model that utilizes features obtained from multiple channels to focus on all the elements within an image using channel and spatial attention. We utilized supervised contrastive learning to classify patient and control groups solely from CDT images. The features obtained from the multiple channels of the MCC-net were used to compute contrastive loss and learn representations of the data. Subsequently, a classifier was trained to learn the decision boundaries between the data. When the MCC-net was trained for binary classification, the accuracy, sensitivity, specificity, and area under the curve reached their maximum values of 0.9718, 0.8358, 0.9789, and 0.9700, respectively. As far as our knowledge extends, this study represents the first instance of utilizing supervised contrastive learning, acquiring features from multiple channels, for classifying CDT images, and we achieved superior performance compared to other models. Furthermore, the model visualized the attention clock elements to provide evidence for the inference results and presents the potential of utilizing artificial intelligence to classify CDT images.
Related Topics
Concepts
Computer science
Artificial intelligence
Convolutional neural network
Pattern recognition (psychology)
Pooling
Classifier (UML)
Focus (optics)
Contextual image classification
Channel (broadcasting)
Binary classification
Machine learning
Support vector machine
Image (mathematics)
Computer network
Optics
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3436102
- OA Status
- gold
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401163197
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4401163197Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3436102Digital Object Identifier
- Title
-
Classification Model of Clock Drawing Test Based on Contrastive Learning Using Multi-Channel Features With Channel-Spatial AttentionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
-
ChangSu Kang, Bohyun Wang, Joon S. LimList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2024.3436102Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/access.2024.3436102Direct OA link when available
- Concepts
-
Computer science, Artificial intelligence, Convolutional neural network, Pattern recognition (psychology), Pooling, Classifier (UML), Focus (optics), Contextual image classification, Channel (broadcasting), Binary classification, Machine learning, Support vector machine, Image (mathematics), Computer network, Optics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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42Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 42 |
| abstract_inverted_index.a | 6, 28, 43, 69, 130 |
| abstract_inverted_index.As | 170 |
| abstract_inverted_index.We | 92 |
| abstract_inverted_index.an | 85 |
| abstract_inverted_index.as | 15, 172 |
| abstract_inverted_index.be | 49 |
| abstract_inverted_index.by | 35 |
| abstract_inverted_index.in | 54, 64 |
| abstract_inverted_index.is | 5 |
| abstract_inverted_index.of | 27, 114, 126, 163, 182, 224 |
| abstract_inverted_index.on | 21, 42, 80 |
| abstract_inverted_index.to | 40, 51, 78, 97, 119, 134, 202, 213, 228 |
| abstract_inverted_index.we | 67, 197 |
| abstract_inverted_index.CDT | 55, 105, 194, 230 |
| abstract_inverted_index.The | 0, 24, 107 |
| abstract_inverted_index.all | 58, 81 |
| abstract_inverted_index.and | 17, 89, 100, 123, 154, 167, 196, 220 |
| abstract_inverted_index.are | 60 |
| abstract_inverted_index.can | 10, 48 |
| abstract_inverted_index.far | 171 |
| abstract_inverted_index.for | 147, 192, 216 |
| abstract_inverted_index.our | 173 |
| abstract_inverted_index.the | 82, 111, 115, 127, 136, 140, 143, 150, 157, 179, 206, 209, 217, 222 |
| abstract_inverted_index.was | 132, 145 |
| abstract_inverted_index.Test | 3 |
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| abstract_inverted_index.When | 142 |
| abstract_inverted_index.area | 155 |
| abstract_inverted_index.from | 75, 104, 110, 189 |
| abstract_inverted_index.loss | 122 |
| abstract_inverted_index.such | 14 |
| abstract_inverted_index.that | 9, 71 |
| abstract_inverted_index.this | 65, 176 |
| abstract_inverted_index.used | 118 |
| abstract_inverted_index.were | 117 |
| abstract_inverted_index.(CDT) | 4 |
| abstract_inverted_index.(CNN) | 32 |
| abstract_inverted_index.Clock | 1 |
| abstract_inverted_index.based | 20 |
| abstract_inverted_index.clock | 211 |
| abstract_inverted_index.curve | 158 |
| abstract_inverted_index.data. | 128, 141 |
| abstract_inverted_index.first | 180 |
| abstract_inverted_index.focus | 41, 79 |
| abstract_inverted_index.image | 86 |
| abstract_inverted_index.learn | 124, 135 |
| abstract_inverted_index.model | 70, 207 |
| abstract_inverted_index.other | 203 |
| abstract_inverted_index.study | 177 |
| abstract_inverted_index.tends | 39 |
| abstract_inverted_index.their | 160 |
| abstract_inverted_index.under | 156 |
| abstract_inverted_index.using | 87 |
| abstract_inverted_index.where | 57 |
| abstract_inverted_index.which | 38 |
| abstract_inverted_index.binary | 148 |
| abstract_inverted_index.detect | 11 |
| abstract_inverted_index.groups | 102 |
| abstract_inverted_index.layers | 26 |
| abstract_inverted_index.neural | 30 |
| abstract_inverted_index.single | 44 |
| abstract_inverted_index.solely | 103 |
| abstract_inverted_index.study, | 66 |
| abstract_inverted_index.values | 162 |
| abstract_inverted_index.within | 84 |
| abstract_inverted_index.0.8358, | 165 |
| abstract_inverted_index.0.9700, | 168 |
| abstract_inverted_index.0.9718, | 164 |
| abstract_inverted_index.0.9789, | 166 |
| abstract_inverted_index.Drawing | 2 |
| abstract_inverted_index.MCC-net | 116, 144 |
| abstract_inverted_index.between | 139 |
| abstract_inverted_index.channel | 88 |
| abstract_inverted_index.compute | 120 |
| abstract_inverted_index.control | 101 |
| abstract_inverted_index.images, | 56, 195 |
| abstract_inverted_index.images. | 106, 231 |
| abstract_inverted_index.maximum | 161 |
| abstract_inverted_index.models. | 204 |
| abstract_inverted_index.network | 31 |
| abstract_inverted_index.patient | 99 |
| abstract_inverted_index.pooling | 25 |
| abstract_inverted_index.provide | 214 |
| abstract_inverted_index.reached | 159 |
| abstract_inverted_index.results | 219 |
| abstract_inverted_index.scoring | 22 |
| abstract_inverted_index.spatial | 90 |
| abstract_inverted_index.trained | 133, 146 |
| abstract_inverted_index.achieved | 198 |
| abstract_inverted_index.channels | 77, 113 |
| abstract_inverted_index.classify | 98, 229 |
| abstract_inverted_index.compared | 201 |
| abstract_inverted_index.compress | 33 |
| abstract_inverted_index.decision | 137 |
| abstract_inverted_index.dominant | 45 |
| abstract_inverted_index.element. | 46 |
| abstract_inverted_index.elements | 59, 83, 212 |
| abstract_inverted_index.evidence | 215 |
| abstract_inverted_index.extends, | 175 |
| abstract_inverted_index.features | 34, 73, 108, 188 |
| abstract_inverted_index.instance | 181 |
| abstract_inverted_index.learning | 96 |
| abstract_inverted_index.multiple | 76, 112, 190 |
| abstract_inverted_index.obtained | 74, 109 |
| abstract_inverted_index.presents | 221 |
| abstract_inverted_index.reducing | 36 |
| abstract_inverted_index.superior | 199 |
| abstract_inverted_index.utilized | 93 |
| abstract_inverted_index.utilizes | 72 |
| abstract_inverted_index.accuracy, | 151 |
| abstract_inverted_index.acquiring | 187 |
| abstract_inverted_index.attention | 210 |
| abstract_inverted_index.channels, | 191 |
| abstract_inverted_index.cognitive | 12 |
| abstract_inverted_index.criteria. | 23 |
| abstract_inverted_index.developed | 68 |
| abstract_inverted_index.diseases, | 19 |
| abstract_inverted_index.features. | 62 |
| abstract_inverted_index.inference | 218 |
| abstract_inverted_index.knowledge | 174 |
| abstract_inverted_index.learning, | 186 |
| abstract_inverted_index.potential | 223 |
| abstract_inverted_index.utilizing | 183, 225 |
| abstract_inverted_index.Therefore, | 63 |
| abstract_inverted_index.artificial | 226 |
| abstract_inverted_index.attention. | 91 |
| abstract_inverted_index.boundaries | 138 |
| abstract_inverted_index.classifier | 131 |
| abstract_inverted_index.represents | 178 |
| abstract_inverted_index.supervised | 94, 184 |
| abstract_inverted_index.visualized | 208 |
| abstract_inverted_index.classifying | 193 |
| abstract_inverted_index.compressing | 52 |
| abstract_inverted_index.contrastive | 95, 121, 185 |
| abstract_inverted_index.detrimental | 50 |
| abstract_inverted_index.examination | 8 |
| abstract_inverted_index.information | 53 |
| abstract_inverted_index.performance | 200 |
| abstract_inverted_index.significant | 61 |
| abstract_inverted_index.Furthermore, | 205 |
| abstract_inverted_index.impairments, | 13 |
| abstract_inverted_index.intelligence | 227 |
| abstract_inverted_index.professional | 7 |
| abstract_inverted_index.sensitivity, | 152 |
| abstract_inverted_index.specificity, | 153 |
| abstract_inverted_index.Subsequently, | 129 |
| abstract_inverted_index.convolutional | 29 |
| abstract_inverted_index.respectively. | 169 |
| abstract_inverted_index.classification, | 149 |
| abstract_inverted_index.dimensionality, | 37 |
| abstract_inverted_index.representations | 125 |
| abstract_inverted_index.Alzheimer’s | 18 |
| abstract_inverted_index.Parkinson’s | 16 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.1510369 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |