Feature Extraction Of Ultrasound Prostate Image Using Modified Vgg-19 Transfer Learning

Abstract

Feature extraction plays a vital role in classification, clustering, diagnosis, and recognition. For the classification process, feature extraction is very important to embody the content of images accurately. The extraction of features from Transrectal Ultrasound (TRUS) is complex as it contains speckle noise, low dissimilarity, the fuzzy region between the object and background. To solve this problem, Ultrasound (US) prostate images are preprocessed and segmented by the Ant Colony Optimization-Boundary Complete Recurrent Neural Network (ACO-BCRNN) method. To extract the relevant features many techniques are applied by the researchers. Recently, transfer learning methods are used for feature extraction. Transfer learning is a machine learning technique in which a trained model in one problem is used in the development of another related problem. Transfer learning consists of various pre-trained models such as VGG16 (Visual Geometry Group), VGG19, Resnet50, InceptionV3, Xception, MobileNetV2, DenseNet, and ResNetV2. The modified VGG-19 model is proposed to extract the features of the prostate ultrasound image. The proposed method was compared with other pre-trained models and its performance is evaluated by using Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Grid Search. The experimental results demonstrated that the pre-trained model VGG19 is superior to other models in terms of Precision, Recall, Accuracy, and F1 Score