Investigation of the efficiency of an interconnected convolutional neural network by classifying medical images

Lantang, Oktavian and Terdik, György and Hajdú, András and Tiba, Attila (2021) Investigation of the efficiency of an interconnected convolutional neural network by classifying medical images. Annales Mathematicae et Informaticae, 53. pp. 219-234. ISSN 1787-6117

Preview

Text AMI_53_from219to234.pdf - Published Version Download (2MB) | Preview

Abstract

Convolutional Neural Network (CNN) for medical image classification has produced satisfying work [11, 12, 15]. Several pretrained models such as VGG19 [17], InceptionV3 [18], and MobileNet [8] are architectures that can be relied on to design high accuracy classification models. This work investigates the performance of three pretrained models with two methods of training. The first method trains the model independently, meaning that each model is given an input and trained separately, then the best results are determined by majority voting. In the second method the three pretrained models are trained simultaneously as interconnected models. The interconnected model adopts an ensemble architecture as is shown in [7]. By training multiple CNNs, this work gives optimum results compared to a single CNN. The difference is that the three subnetworks are trained simultaneously in an interconnected network and showing one expected result. In the training process the interconnected model determines each subnetwork’s weight by itself. Furthermore, this model will apply the most suitable weight to the final decision. The interconnected model showed comparable performance after training on several datasets. The measurement includes comparing the Accuracy, Precision and Recall scores as is shown in confusion matrix [3, 14].

Actions (login required)

Edit Item