Automatic Thyroid Ultrasound Image Classification Using Deep Learning
MRS.P.SHANTHI
Associate Professor, Dept. of Information Technology, Sreenidhi Institute of Science and Technology
shanthip@sreenidhi.edu.in
DR. K. KRANTHI KUMAR
Associate Professor, Dept. of Information Technology, Sreenidhi Institute of Science and Technology
kranthikumark@sreenidhi.edu.in
S.SHIVA PRASAD YADAV
B.Tech Student, Dept. of Information Technology, Sreenidhi Institute of Science and Technology
20315a1207@sreenidhi.edu.in
S.NAVEEN KUAMR
B.Tech Student, Dept. of Information Technology, Sreenidhi Institute of Science and Technology
20315a1210@sreenidhi.edu.in
M.MOHAN SAI
B.Tech Student, Dept. of Information Technology, Sreenidhi Institute of Science and Technology
20315a1212@sreenidhi.edu.in
Abstract - At the moment, identifying thyroid nodules is mostly done through clinical procedures that need a large workforce and plenty of medical materials. As a result, this study recommends an automated method that combines convolutional neural networks with information about picture texture for identifying thyroid ultrasound nodules. The initial steps are as follows: The collection of both positive and negative samples, image standardisation, and nodule region segmentation are the initial steps in the creation of an ultrasound thyroid nodule dataset. In the subsequent stage, texture features are extracted from the data, features are chosen, and the data's dimensionality is decreased in order to produce a texture features model. Finally, in transfer learning, a feature model of the nodule in pictures is created using deep neural networks. A new nodular feature model called the Feature Fusion Network is created by fusing the texture feature model with the convolutional neural network feature model. The final option is feature fusion. Through the use of a single network for training and performance improvement, a deep neural network diagnosis model is created that can change to fit the features of thyroid nodules. For the purpose of researching this approach, 1874 groups of thyroid nodules discovered by clinical ultrasonography were gathered. Based on Precision and Recall, the harmonic average F-score is used to measure assessment. Feature Fusion Network has an F-score of 92.52% for differentiating between benign and malignant thyroid nodules, according to the experimental data. Our strategy outperforms traditional machine learning techniques as well as convolutional neural networks.
