Identification of thyroid nodules in infrared images by convolutional neural networks

Early detection of thyroid anomalies decreases the chances of disease progression. Imaging examinations consist in an important tool in the diagnostic process. However, most of them are relatively expensive or can expose the patient to excessive radiation. Thermography is an interesting alternative in thyroid diseases diagnosis, especially in the detection of nodules, since some of them tend to present higher temperatures than normal tissues. Image processing techniques can be used to find regions that may indicate thyroid nodules. To select which one of these regions are in fact related to a nodule, a Convolutional Neural Network - CNN can be used. CNNs are widely used in clinical images classification, and some models have shown good results in this kind of problem. In this work, we present a methodology to identify thyroid nodules in thermograms by using simple image processing techniques and CNNs. Three CNNs were tested, the first one based in the GoogLeNet architecture, a second based in the AlexNet and a third one based in the VGG architecture. The GoogLeNet CNN yielded the highest accuracy (86.22%) followed by AlexNet (77.67%) and the VGG (74.96%).