TY - JOUR N2 - In recent years, deep learning and especially deep neural networks (DNN) have obtained amazing performance on a variety of problems, in particular in classification or pattern recognition. Among many kinds of DNNs, the convolutional neural networks (CNN) are most commonly used. However, due to their complexity, there are many problems related but not limited to optimizing network parameters, avoiding overfitting and ensuring good generalization abilities. Therefore, a number of methods have been proposed by the researchers to deal with these problems. In this paper, we present the results of applying different, recently developed methods to improve deep neural network training and operating. We decided to focus on the most popular CNN structures, namely on VGG based neural networks: VGG16, VGG11 and proposed by us VGG8. The tests were conducted on a real and very important problem of skin cancer detection. A publicly available dataset of skin lesions was used as a benchmark. We analyzed the influence of applying: dropout, batch normalization, model ensembling, and transfer learning. Moreover, the influence of the type of activation function was checked. In order to increase the objectivity of the results, each of the tested models was trained 6 times and their results were averaged. In addition, in order to mitigate the impact of the selection of learning, test and validation sets, k-fold validation was applied. L1 - http://journals.pan.pl/Content/112085/PDF/21_363-376_00946_Bpast.No.67-2_06.02.20.pdf L2 - http://journals.pan.pl/Content/112085 PY - 2019 IS - No. 2 EP - 376 DO - 10.24425/bpas.2019.128485 KW - deep neural network KW - deep learning KW - image classification KW - batch normalization KW - transfer learning KW - dropout A1 - Grochowski, M. A1 - Kwasigroch, A. A1 - Mikołajczyk, A. VL - 67 DA - 30.04.2019 T1 - Selected technical issues of deep neural networks for image classification purposes SP - 363 UR - http://journals.pan.pl/dlibra/publication/edition/112085 T2 - Bulletin of the Polish Academy of Sciences Technical Sciences ER -