Analysis of thyroid nodule ultrasound images by image feature extraction technique

Abstract

The most frequent left thyroid nodule is the presence of thyroid nodules that have never been seen before. With X-ray computed tomography (CT) being used more often in diagnosing thyroid disorders, however, image processing has not been applied frequently to standard machine learning due to the high density and artefacts found in CT images of the thyroid gland. The last section suggests a Convolutional Neural Network (CNN)-based end-to-end approach for automatic detection and classification of different types of thyroid nodules. The recommended model includes an improved segmentation network that effectively divides the regions within which each nodule may be detected and an image processing technique that optimizes these areas. For example, 98% accuracy was obtained in accurately categorising illness cases by examining aberrant modules of X-rays. According to our study, CNN can accurately detect different degrees of severity caused by nodules located in various parts of the body, thereby providing a means through which this procedure can be done automatically without requiring human intervention all the time. Overall, this study demonstrates how deep learning models may be used to automatically identify and diagnose thyroid nodules using CT imaging, which could increase the precision and effectiveness of diagnosing thyroid disease.