Thermography based skin allergic reaction recognition by convolutional neural networks

Abstract

In this work we present an automated approach to allergy recognition based on neural networks. Allergic reaction classification is an important task in modern medicine. Currently it is done by humans, which has obvious drawbacks, such as subjectivity in the process. We propose an automated method to classify prick allergic reactions using correlated visible-spectrum and thermal images of a patient’s forearm. We test our model on a real-life dataset of 100 patients (1584 separate allergen injections). Our solution yields good results—0.98 ROC AUC; 0.97 AP; 93.6% accuracy. Additionally, we present a method to segment separate allergen injection areas from the image of the patient’s forearm (multiple injections per forearm). The proposed approach can possibly reduce the time of an examination, while taking into consideration more information than possible by human staff.

Skin Prick Automated Test device offers more reliable allergy test results compared to a manual skin prick test

“…It was concluded that the agreement between the device and the manual procedure was moderate (25) . Other methods supporting the automated read out of the SPT results are based on 3D imaging (26) or are using a combination of visible-spectrum and thermal images (27) .…”

Use of Infrared Thermal Imaging for Assessing Acute Inflammatory Changes: A Case Series

“…The lower panels of Figure 2 show how the inflammatory changes reverted after antihistamine administration. The use of IRT for assessing allergy skin prick tests demonstrated a sensitivity of 72%-93% and a specificity of 60%-88%; thus, this imaging modality can be used to automatize and objectivize the test procedure particularly when paired with computer visioning AI techniques [ 12 , 13 ].…”