Early Identification and Classification of Thyroid Nodule in Medical Ultrasound Images

Abstract

In medical imaging, machine learning (ML) and deep learning (DL) play a significant role to predict symptoms of early diseases. DL is one of the growing trends in general data analysis since 2013. It is an improvement of artificial neural networks (ANN) as it consists of many hidden layers to permit high level of abstraction from data. In particular, convolutional neural networks (CNNs) have proven to be a potential tool for computer vision tasks. Deep CNN shave a capability to automatically learn raw data especially images. The accurate assessment or identification of disease depends on image interpretation as well as acquisition. Due to the improvement in last decade in image acquisition, devices acquire data at high rate with increased resolution. However, the interpretation of images has recentlybeguntobenefitfromcomputertechnology.Mostlythesearemadebytheradiologist, physicians and senior doctors but limited due to its subjectivity and high skilled physicians/doctors. Computerized tools in the medical imaging field are the key enablers to improve diagnosis by facilitating the findings. Analysis of thyroid ultrasonography (USG)images via visual inspection and manual examination for early identification and classification of thyroid nodule has always been cumbersome. This manual examination of thyroid USG images in order to identify benign and malignant thyroid nodule can be tedious and time-consuming. Various deep learning models have emerged in medical field especially in thyroid nodule classification with the rapid advancement in technology and increase in computational resources. Early identification of such nodules can improve the effectiveness of clinical interventions and treatments. Therefore, many researchers now advocate the use of computer diagnostic system (CDS) to objectively and quantitatively analyze the USG images of thyroid nodules. This helps the radiologists to solve the differences in interpretation of results.