A hybrid model for the identification and classification of thyroid nodules in medical ultrasound images

Ultrasonography (USG) is one of the leading diagnostic methods for accurately distinguishing the early-stage of thyroid nodules. ANN-SVM hybrid model is proposed for the identification and classification of thyroid nodules in medical ultrasound images. After feature extraction using grey level co-occurrence matrix method, two experiments are performed. In the experiment-1, five different machine learning (ML) classifiers like random forest (RF), support vector machine (SVM), decision tree (DT), artificial neural network (ANN) and K-nearest neighbour (KNN) are used for classification. While in experiment-2, the two best classifiers based on the performance are hybrid together. The proposed hybrid model has achieved 84.12% accuracy, 85.14% sensitivity and 82.95% specificity on the public dataset having 295 USG images and 90% accuracy, 91.66% sensitivity and 87.5% specificity on the local dataset having 654 thyroid USG images. It has shown an improvement of 2% to 5% in the performance evaluation in comparison with the other state-of-the-art methods.