A review on Deep Learning in thyroid ultrasound Computer-Assisted Diagnosis systems

Ultrasound is one of the most used imaging techniques for assessing and evaluating thyroid lesions. Indeed, it shows a good performance in terms of discrimination between benign and malignant thyroid nodules. Diagnosis by ultrasound is, however, not as easy as it seems and depends strongly on the experience of the radiologists. To help physician and radiologists to better diagnose, many Computer-Assisted Diagnosis (CAD) systems have been developed. These systems are based on image processing techniques and on machine learning. They represent effective and useful tools allowing doctors to have a second opinion far from human subjectivity. Among the machine learning techniques, the Deep Learning has recently made rapid progress in interpreting medical imaging and has demonstrated an impressive efficiency. Various CAD systems treating ultrasound images of the thyroid have widely use it since then. This paper reviews the most recent research works on the CAD systems for analyzing ultrasound images of the thyroid to diagnose the benign or malignant nature of the thyroid nodules. The CAD systems studied in this paper are based on the Deep Learning. We present a brief description of the CAD systems based on the Deep Leaning. Specifically, we describe the data collection and the CNN implementation. We report also the results obtained in these studies and highlight the limitations of such studies. This literature review is aimed at researchers but also at physician who are interested in CAD tools in ultrasound images of the thyroid gland and can represent a state of the art for all those interested in the classification of medical imaging.