# Deep Learning Methods for Bug Bite Classification: An End-to-End System
###### Abstract  
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. image classification; deep neural networks; transfer learning; classifier fusion; mobile application; bug bites +
Footnote †: journal:restrictions. To some extent, this procedure may be simplified in dermatology due to the easy, fast, safe and non-invasive possibility to collect data. Access to publicly available dermatological datasets opens the possibility for the application of machine learning methods, facilitates the analysis of different skin manifestations and supports reproducible research. Many known results are based on publicly available datasets. For example, the authors of [10] used both public domain images and clinical images. They discussed deep learning models for the detection of the characteristic skin rash from Lyme disease compared to other clinically relevant skin lesions. Different machine learning methods have been proposed for the classification of a range of skin diseases, such as cancers, autoimmune diseases, infections, rushes, etc. (see [11; 12; 13; 14; 15; 16] and references therein). However, to the best of our knowledge, there are limited results in the literature referring to the classification of arthropod bites, specifically bites and stings from insects and arachnids. The goal of this paper is to fill in this gap. Most bug bites may result only in localized reactions and present only minor symptoms, such as redness, swelling, rashes, or itching. An allergic reaction may be triggered by mosquitoes, bed bugs, fire ants, bees, wasps, and kissing bugs. A prolonged allergic or inflammatory reaction may need pharmacological treatment, and a severe allergic response warrants immediate medical attention. Allergic symptoms may also appear after spider bites, and in some parts of the world there is a possibility of venomous exposure. Spider bites increase the risk for contracting tetanus, and in some cases, tetanus boosters may be recommended. Infected insects may transmit vector-borne diseases, many of which can be life threatening.