Abstract
Image-based computer-aided diagnosis (CAD) systems have been developed to assist doctors in the diagnosis of thyroid cancer using ultrasound thyroid images. However, the performance of these systems is strongly dependent on the selection of detection and classification methods. Although there are previous researches on this topic, there is still room for enhancement of the classification accuracy of the existing methods. To address this issue, we propose an artificial intelligence-based method for enhancing the performance of the thyroid nodule classification system. Thus, we extract image features from ultrasound thyroid images in two domains: spatial domain based on deep learning, and frequency domain based on Fast Fourier transform (FFT). Using the extracted features, we perform a cascade classifier scheme for classifying the input thyroid images into either benign (negative) or malign (positive) cases. Through expensive experiments using a public dataset, the thyroid digital image database (TDID) dataset, we show that our proposed method outperforms the state-of-the-art methods and produces up-to-date classification results for the thyroid nodule classification problem.
Highlights
The traditional diagnostic technique, based on the expert knowledge of doctors, has a critical limitation in that the result of the diagnosis is heavily dependent on the personal knowledge and experience of the doctor
In contrast to most of the previous studies, which only consider information on thyroid nodule images in the spatial domain, our study explores the utility of information in the frequency domain for the thyroid nodule classification problem, using the Fast Fourier Transform (FFT) method, based on our observation of the characteristics of benign and malign nodules
IdnetehpisCSNecNtionne,twweoprkress. ent various experiments using our proposed method mentioned in Section 2 in comparison with various previous methods using the thyroid digital image database (TDID) dataset
Summary
The traditional diagnostic technique, based on the expert knowledge of doctors, has a critical limitation in that the result of the diagnosis is heavily dependent on the personal knowledge and experience of the doctor. The performance of diagnosis is limited and varies with the doctor’s experience To surmount this limitation, a double screening scheme has been applied in some hospitals by employing an additional expert [1]. The CAD system serves as the additional expert in the double screening scheme and makes suggestions to doctors during diagnosis of diseases This diagnosis technique is based on captured images of several parts of the human body and a computer-based program for identifying abnormal signs in these parts in lieu of the professional knowledge of doctors. We focus on the classification of benign and malign cases of thyroid nodules
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
