Enhanced DBN-based Thyroid Segmentation and Classification of US Images using Coyote Optimization Algorithm

In this paper, a novel deep learning method is introduced for accurately segmenting and classiMng the thyroidL Here, the dataset is gathered from the standard sources. Next, the median filtering performs the preprocessing process. Further, the watershed segmentation is performedL Additionally, GLCM is used for extracting the features. In the final step, the classification is accomplished by the enhanced DBN, where the hidden neurons of DBN are optimized by COA thus known to be enhanced DBN. This enhanced DBN classifies the final output into normal and abnormal images. Simulation findings demonstrate the superiority of the proposed model.