Optimization assisted convolutional neural network for detection of thyroid

Abstract

This research work endeavor to suggest a new thyroid detection system that involves three main processes like segmentation, feature extraction, and detection. Initially, for the segmentation process, an improved watershed algorithm is proposed. Besides, from the segmented image, it extracted the texture features that comprises local binary patterns, modified GLCM feature, local tetra pattern features, together with the statistical features. Primarily, the extracted features are given as an input to convolutional neural network (CNN) for the final detection result. For making the detection more accurate, the training of deep CNN is performed via a new deer encircling included gray wolf optimization model for optimal weights tuning. The adopted hybrid algorithm combines gray wolf optimization and deer hunting optimization algorithm. At last, the outcomes of the adopted scheme is validated with the extant approaches under various metrics.