The workup of these incidental thyroid nodules typically includes sonography. As the majority of these nodules are benign or behave indolently, accurate determination of whether thyroid nodules are benign or malignant could reduce patient risk and reduce the significant medical health care costs with fine needle analysis (FNA) biopsy and/or surgery. 16), while 96.34% classification accuracy, 86% sensitivity and 99% specificity for the images in our local health region database.Дetection of thyroid nodules (TNs) has significantly increased over the past two decades with many more nodules now being incidentally detected.

The experimental results show the proposed fine-tuned GoogLeNet model achieves excellent classification performance, attaining 98.29% classification accuracy, 99.10% sensitivity and 93.90% specificity for the images in an open access database (Pedraza et al. The extracted features of the thyroid ultrasound images are sent to a Cost-sensitive Random Forest classifier to classify the images into вЂњmalignantвЂќ and вЂњbenignвЂќ cases. A pre-trained GoogLeNet model is then fine-tuned using the pre-processed image samples which leads to superior feature extraction. Ultrasound images are pre-processed to calibrate their scale and remove the artifacts. We use deep learning approach to extract features from thyroid ultrasound images. This paper presents a computer-aided diagnosis (CAD) system for classifying thyroid nodules in ultrasound images. With many thyroid nodules being incidentally detected, it is important to identify as many malignant nodules as possible while excluding those that are highly likely to be benign from fine needle aspiration (FNA) biopsies or surgeries.

Finetune googlenet caffe