Diagnosis of anomalies in Thyroid Gland Images Based on Feature Extraction from Capsule Network Architecture.

Subject Areas : Journal of Computer & Robotics

Mahin  Tasnimi ¹ (Department of Computer, Ferdows Branch, Islamic Azad University, Ferdows , Iran)
Hamid Reza  Ghaffari ² (Department of Computer, Ferdows Branch, Islamic Azad University, Ferdows , Iran)

Received: 2021-07-02 Accepted : 2021-10-07 Published : 2021-07-01

Keywords: deep learning, convolutional neural network (CNN), Thyroid Gland, Feature Extract, Capsule Network,

Abstract :

Diagnosing benign and malignant glands in thyroid ultrasound images is considered as a challenging issue. Recently, deep learning techniques have significantly resulted in extracting features from medical images and classifying them. Convolutional networks ignore the hierarchical structure of entities within images and do not pay attention to spatial information as well as the need for a large number of training samples. Capsule networks consist of different hierarchical capsules equivalent to the same layers in the CNN neural network. This study tried to extract textural features using a deep learning model based on a capsule network. Thyroid ultrasound images were given to the capsule network as input data, and finally the features learned in the capsule network were used to teach the Support Vector Machine classifier, in order to diagnose thyroid cancer. Experimental results showed that the proposed method with 98% accuracy has achieved better results compared to convolutional networks.