20.500.12556/RUL-124243 The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset Many hyperparameters have to be tuned to have a robust convolutional neural network that will be able to accurately classify images. One of the most important hyperparameters is the batch size, which is the number of images used to train a single forward and backward pass. In this study, the effect of batch size on the performance of convolutional neural networks and the impact of learning rates will be studied for image classification, specifically for medical images. To train the network faster, a VGG16 network with ImageNet weights was used in this experiment. Our results concluded that a higher batch size does not usually achieve high accuracy, and the learning rate and the optimizer used will have a significant impact as well. Lowering the learning rate and decreasing the batch size will allow the network to train better, especially in the case of fine-tuning. true false false Angleški jezik Angleški jezik Članek v reviji 2021-01-12 11:24:15 2021-01-12 11:27:51 2022-09-04 03:58:37 0000-00-00 00:00:00 2020 0 0 Str. 312-315 iss. 4 Vol. 6 2020 0000-00-00 Zaloznikova NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 681.5 2405-9595 10.1016/j.icte.2020.04.010 38422787 526132505 RAZ_Kandel_Ibrahem_2020.pdf RAZ_Kandel_Ibrahem_2020.pdf 1 FDA0963A73CA0DDBA6BD9414D0C9463F 3299a21196671dec02a38c32aa311cda0a16b6b5d5fa4b596cc73e3c34c2fce0 8442958d-a1bb-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=139662 Ekonomska fakulteta 0 0 0