Automatic detection and diagnosis of thyroid ultrasound images based on attention mechanism

• Published in: Computers in biology and medicine 2023-03, Vol.155, p.106468-106468, Article 106468
• Main Authors: Yu, Zhenggang, Liu, Shunlan, Liu, Peizhong, Liu, Yao
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Attention mechanisms; Biopsy; Classification; Clinical medicine; Computer-aided diagnosis; Data systems; Deep learning; Diagnosis; Diagnosis, Computer-Assisted - methods; Diagnostic systems; Humans; Machine learning; Medical diagnosis; Medical imaging; Medical research; Methods; Morbidity; Neural networks; Nodules; Thyroid; Thyroid cancer; Thyroid Neoplasms; Thyroid Nodule; Thyroid nodules; Ultrasonic imaging; Ultrasonography - methods; Ultrasound
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.106468

Incidents of thyroid cancer have dramatically increased in recent years; however, early ultrasound diagnosis can reduce morbidity and mortality. The work in clinical situations relies heavily on the subjective experience of the sonographer. Numerous computer-aided diagnostic techniques exist, but most consider how good the results are, ignoring the pre-image collecting and its usefulness in post-clinical practise. To address these issues, this study proposes a computer-aided diagnosis method based on an attentional mechanism. Due to its lightweight properties, the model can rapidly identify nodules and distinguish between benign and malignant ones without using much hardware. The model uses a bounding box to locate the thyroid nodule and determines whether it is benign or cancerous, and outputs the diagnostic result of the thyroid nodule ultrasound images. The latest attention mechanisms are used to get better results at a fraction of the cost. Additionally, ultrasound images with different features of benign and malignant thyroid nodules were collected following the Thyroid Imaging Reporting and Data System standards. The experimental results showed that the approach identifies and classifies thyroid nodules rapidly and effectively; the mAP value of the results reached 0.89, and the mAP value of malignant nodules reached 0.94, with detection rate of single image reached 7 ms. Young physicians and small hospitals with limited resources can benefit from using this method to assist with thyroid ultrasound examination diagnosis. •A deep learning model based on the attention mechanism is proposed, which can distinguish between benign and malignant thyroid nodules with high accuracy, especially for malignant nodules.•A light-weight thyroid nodule recognition network is proposed, which can accurately identify and frame thyroid nodules at a fast speed and has practical effects in clinical applications.•A data set based on the TI-RADS standard was constructed, and all the data were consistent with the characteristics of benign and malignant thyroid nodules, which can better guide the model to distinguish between benign and malignant nodules.