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At the time of publication, Dengue Advisory Techniques (DAT) is a live broadcast software from the U.S. Centers for Disease Control and Prevention (CDC) that uses deep learning models to detect and track outbreak situations rapidly and accurately. This tool could monitor the spread of the virus and identify areas where alerts might be raised. In this work, we evaluate the use of visual metaphor in a DAT-based Dengue software to improve detection performance. To develop the software, we used deep learning technique to generate anomaly detectors, or classification models, which are trained on a large number of positive and negative data samples. However, in case of anomaly detection, there is a need to train detectors on a large set of negative data samples as well. But visually dissimilar images of different categories have almost identical training data—even when the images vary slightly. This is to be avoided, as such dissimilar images are likely to be classified as positive data samples. In fact, most anomaly detection algorithms are not scale invariant, i.e., they are trained on a single image dataset, which is not sufficient to ensure high detection accuracy. In an approach termed as visual metaphor, the increasing popularity of deep learning has been used to create labeled datasets of visual data that are able to teach other models to recognize patterns that they cannot discover. Visual metaphor research helps researchers to facilitate the development of a powerful student model through free-form explanation and effective visualization operations on visual data [119, 120]. This work includes visual annotation of features on a large dataset.) Figure 1: High-Quality Visual Metaphor Dataset for Dengue Prediction We collected the Dengue images using Google image search as our main data source, where we have used the first 200 results that include the word “dengue”. It is worth noting that the use of Google for data collection is valid in the sense that Google is the largest search engine on the web after Microsoft [4]. However, as our main data source, Google is limited as it can only provide relevant images. For instance, it does not show textual data over images. That is, in step 1, for each image, we obtained its words by using PIL PyImage. The “words” of each image contain three numbers (pixel coordinates).

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