A bite from a bug may expose the affected person to serious, life-threatening conditions, which may require immediate medical attention. The identification of the bug bite may be challenging even for experienced medical personnel due to the different manifestations of the bites and similarity to other skin conditions. This motivated our work on a computer-aided system that offers information on the bug bite based on the classification of bug bite images. Recently, there have been significant advances of methods for image classification for the detection of various skin conditions. However, there are very few sources that discuss the classification of bug bites. The goal of our research is to fill in this gap in the literature and offer a comprehensive approach for the analysis of this topic. This includes (1) the creation of a dataset that is larger than those considered in the related sources; (2) the exploration and analysis of the application of pre-trained state-of-the-art deep learning architectures with transfer learning, used in this study to overcome the challenges of low-size datasets and computational burden; (3) the further improvement of the classification performance of the individual CNNs by proposing an ensemble of models, and finally, (4) the implementation and description of an end-to-end system for bug bite classification from images taken with mobile phones, which should be beneficial to the medical personnel in the diagnostic process. In this paper, we give a detailed discussion of the models’ architecture, back-end architecture, and performance. According to the general evaluation metrics, DenseNet169 with an accuracy of 78% outperformed the other individual CNN models. However, the overall best performance (accuracy of 86%) was achieved by the proposed stacking ensemble model. These results are better than the results in the limited related work. Additionally, they show that deep CNNs and transfer learning can be successfully applied to the problem of the classification of bug bites.

Background and Objective: This study aims to investigate the prevalence of systemic and ophthalmic manifestations in different refractive groups in children and young adults with Down syndrome (DS). Materials and Methods: The study was a population-based, cross-sectional study that included 141 Caucasian children and young adults with DS. They were classified into the following three groups: myopia DS group (37 subjects, mean age 15.8 years), emmetropia DS group (41 subjects, mean age 11.7 years) and hyperopia DS group (63 subjects, mean age 10.9 years). The participants underwent inspection, slit-lamp examination, cycloplegic refraction, ocular alignment and ocular motility examination. Ten systemic manifestations were analyzed. Results: There was no difference in the prevalence of any systemic manifestations between the groups. Considering the ophthalmic manifestations, there was statistical difference in the distribution of proportions among the three groups for nystagmus (p = 0.011), iris-stromal atrophy (p = 0.048) and strabismus (p = 0.031). The prevalence of strabismus in our DS myopia group was 35.1%, and in DS hyperopia group 38.1%. Conclusions: The results of our study suggest that DS children and young adults with any refractive error do not have a higher chance of additional systemic manifestations. Myopia in DS was associated with a higher prevalence of nystagmus and iris stromal atrophy, whereas astigmatism was found to be more frequent in hyperopia.