Thyroid nodules classification and diagnosis in ultrasound images using fine‐tuning deep convolutional neural network. Issue 1 (16th August 2019)

• Record Type: Journal Article
• Authors: Moussa, Olfa; Khachnaoui, Hajer; Guetari, Ramzi; Khlifa, Nawres
• Is Part Of: International journal of imaging systems and technology. Volume 30:Issue 1(2020)
• Page Start: 185
• Page End: 195
• Publication Date: 2019-08-16
• Subjects: computer‐aided diagnosis (CAD) system -- deep convolutional neural network -- deep learning -- fine‐tuning -- thyroid nodule -- ultrasound image; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22363
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Ultrasonography AKA diagnostic sonography is a noninvasive imaging technique that allows the analysis of an organic structure, thanks to the ultrasonic waves. It is a valuable diagnosis method and is also seen as the evidence‐based diagnostic method for thyroid nodules. The diagnosis, however, is visually made by the practitioner. The automatic discrimination of benign and malignant nodules would be very useful to report Thyroid Imaging Reporting. In this paper, we propose a fine‐tuning approach based on deep learning using a Convolutional Neural Network model named resNet‐50. This approach allows improving the effectiveness of the classification of thyroid nodules in ultrasound images. Experiments have been conducted on 814 ultrasound images and the results show that our proposed approach dramatically improves the accuracy of the classification of thyroid nodules and outperforms The VGG‐19 model.