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

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. ∗This work was supported by the construction EFOP-3.6.3-VEKOP-16-2017-00002. The project was supported by the European Union, co-financed by the European Social Fund. Research was also supported by the ÚNKP-20-4-I New National Excellence Program of the Ministry for Innovation and Technology from the Source of the National Research, Development and Innovation Fund, and by LPDP Indonesia in the form of a doctoral scholarship (https://www.lpdp.kemenkeu.go.id). Annales Mathematicae et Informaticae 53 (2021) pp. 219–234 doi: https://doi.org/10.33039/ami.2021.04.001 url: https://ami.uni-eszterhazy.hu