This paper proposes a computer-aided methodology for detecting and segmenting the tumor regions in ultrasound thyroid images using machine and deep learning algorithms. This proposed tumor detection methodology uses Kirsch’s edge detector for enhancing the edge region pixels in thyroid image and then Dual Tree Contourlet Transform (DTCT) was applied on the enhanced image for obtaining the coefficients. Then, features are computed from this transformed thyroid image and these features are trained and classified using the Co-Active Adaptive Neuro Expert System (CANFES) classifier. Then, the morphological segmentation method is applied on the abnormal thyroid image to segment the tumor regions. Finally, the Convolutional Neural Network (CNN) algorithm is applied on the segmented tumor regions for diagnosing them into mild, moderate and severe.

本文提出了一种计算机辅助方法，用于使用机器和深度学习算法检测和分割超声甲状腺图像中的肿瘤区域。这种提出的肿瘤检测方法使用 Kirsch 的边缘检测器来增强甲状腺图像中的边缘区域像素，然后对增强图像应用双树 Contourlet 变换 (DTCT) 以获得系数。然后，根据这个转换后的甲状腺图像计算特征，并使用 Co-Active Adaptive Neuro Expert System (CANFES) 分类器对这些特征进行训练和分类。然后，将形态学分割方法应用于甲状腺异常图像，对肿瘤区域进行分割。最后，将卷积神经网络（CNN）算法应用于分割的肿瘤区域，将其诊断为轻度、中度和重度。