Deep Learning Methods for Bug Bite Classification: An End-to-End System
Journal
Applied Sciences 13
Date Issued
2023
Author(s)
Trojachanec Dineva, Katarina
Tojtovska Ribarski, Biljana
Petrov, Petar
Mladenovska, Teodora
Trajanoska, Milena
Gjorshoska, Ivana
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.
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.
Subjects
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