Abstract
Background: The thyroid size increases in some diseases of this gland, which is known as goiter or thyromegaly. Thyromegaly is more prevalent in women than men. Thyroid ultrasound is a non-invasive, inexpensive, and safe method for assessing thyroid disorders. Length, width (W), and height measuring of the thyroid lobes and volume is a time-consuming process. Objectives: The object of the present study was to offer a rapid artificial neural network (ANN) system for automatic detection of thyromegaly and to investigate the relationship between thyroid dimensions and its volume. Methods: The thyroid dimensions including longitudinal (L) diameter, anterior-posterior or depth (AP) diameter, W diameter, and AP diameter of isthmus were measured among 131 individuals, assigned to thyromegaly and normal groups during the years 2018 and 2019. Proposed ANN system containing of one hidden layer, 3 neurons, and one output for representing the presence of thyromegaly was created by MATALB software. Results: The implementation of the neural network showed that the use of the measured parameters of the right lobe is able to detect thyromegaly with an accuracy of 94.74, and the isthmus information does not increase the accuracy of the network very much. Also, "width of the right lobe" is the most appropriate parameter and the isthmus thickness was the least important parameter for automatic detection of thyromegaly. Conclusions: Artificial neural network as a decision-support system can be useful in reducing measurement error while performing ultrasound in detection of thyromegaly and also reducing time of diagnostic processes
Published Version
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