Evaluation of Quantitative Features and Convolutional Neural Networks for Nodule Identification in Thyroid Thermographies

Thyroid anomalies have high prevalence and their early identification is crucial for a more effective treatment. Thermograms can be used in this process, since nodules tend to be more vascularized, resulting in a different behaviour delated to temperatures on the skin surface over them. This work presents a methodology for determining thyroid nodules. We evaluate features that would allow to segment possibly nodular regions in thermographs. Convolutional neural networks (CNN) are used to classify these regions, identifying which ones refer to nodules. The good results of CNN in the classification (with more than 92% of accuracy), show that the viability of the proposed methodology depends on the success of the segmentation.