HyRZNet: HYBRID RESIDUAL-ZFNet FOR THYROID CANCER CLASSIFICATION USING ULTRASOUND IMAGES

Thyroid cancer is a form of cancer that begins in the thyroid gland. Precise and efficient treatments can be provided for thyroid cancer if nodules are detected early, thereby greatly lowering morbidity and mortality. Early thyroid nodule detection has been achieved by applying the traditional approach based on ultrasound imaging widely. A Deep Learning (DL)-based technique for classifying thyroid cancer is introduced here, as the conventional methods are time-consuming, expensive, and sometimes even ineffective. Hence, a Hybrid Residual Zeiler and Fergus Network (HyRZNet) framework is developed in this research for thyroid cancer classification. Initially, thyroid ultrasound images are obtained from the specified database and are subjected to image pre-processing, where a Bilateral Filter (BF) is employed to eradicate the noise in the image. Later, the pre-processed image is given to cancer-affected region detection using a Mask Region-based Convolutional Neural Network (Mask RCNN). After cancer region detection, a feature extraction process is carried out, where texture features, namely, Complete Local Binary Pattern (CLBP), Local Vector Pattern (LVP), and Grey-level Co-occurrence Matrix (GLCM) features are extracted. Finally, thyroid cancer classification is carried out by HyRZNet and the accuracy obtained is 90.80%, True Positive Rate (TPR) is 92.46%, and True Negative Rate (TNR) is 89.20%. Compared to the existing classifiers, the proposed classifier is more accurate.