Classification Model of Clock Drawing Test Based on Contrastive Learning Using Multi-Channel Features With Channel-Spatial Attention

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.