Skin cancer is a common disease among middle-aged and elderly white-skinned people. It is divided into many types in terms of medical criteria. Malign melanoma is one of the most dangerous and fatal cancer types, it can be treated if detected early. The main focus of this paper is to provide a precise, effective, robust and automated way to segment the lesion in order to facilitate the classification of the lesion with high accuracy during the early diagnosis of skin cancer. This process consists of two stages. In the first stage, image processing techniques (Image Enhancement, Linear Filtering, and Image Restoration) are used to obtain images free of artifacts such as hair and ruler marks. The second stage which is the important part of this paper is to modify a U-Net architecture and propose a 46-layered structure of U-Net to obtain a successful lesion segmentation rate. In this study, experiments were performed on two different U-Net architectures (U-Net 32, and U-Net 46).

The quality of human embryos produced during in vitro fertilization is conventionally graded by clinical embryologists and this process is time-consuming and prone to human error. Artificial intelligence methods may be used to grade images captured by time-lapse microscopy (TLM). Segmentation of embryos from the background of TLM images is an essential step for embryo quality assessment as the background of the embryo has various artifacts which may mislead the grading algorithms. In this study, we performed a comparative analysis of automated day-5 human embryo (blastocyst) image segmentation methods based on deep learning. Four fully convolutional deep models, including U-Net and its three variants, were created using the combination of two gradient descent-based optimizers and two-loss functions and compared to our proposed model. The experimental results on the test set confirmed that our customized Dilated Inception U-Net model with Adam optimizer and Dice loss outperformed other U-Net variants with Dice coefficient, Jaccard index, accuracy, and precision of 98.68%, 97.52%, 99.20%, and 98.52%, respectively.

Digital natives have natural skills of using technology. In spite of that skill, their role, as the producer of digital world, is an arguing point of this study. In order to discuss that point, a training course for children, “Programming with Small Basic”, was designed. The study group is consisted of nineteen children, aged 11 to 14. Dominant-less dominant quantitative-qualitative sequential mixed research method was used in the study. Collected data were analysed by using descriptive analysis, content analysis and Wilcoxon test (SPSS 21.0). The results show that digital natives are good readers of digital world but they need to be supported to become good writers of that world