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.
Keywords: Fast Fourier transform; artificial intelligence; deep learning; frequency domain; spatial domain; thyroid nodule classification.