Subject: image classification; deep neural networks; transfer learning; classifier fusion; mobile application; bug bites
Year: 2023
Type: Article
Title: Deep Learning Methods for Bug Bite Classification: An End-to-End System
Author: Ilijoski, Bojan
Author: Trojachanec Dineva, Katarina
Author: Tojtovska Ribarski, Biljana
Author: Petrov, Petar
Author: Mladenovska, Teodora
Author: Trajanoska, Milena
Author: Gjorshoska, Ivana
Author: Lameski, Petre
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.
Publisher: MDPI
Relation: Applied Sciences 13
Identifier: oai:repository.ukim.mk:20.500.12188/26377
Identifier: http://hdl.handle.net/20.500.12188/26377
