Deep Learning Methods for Bug Bite Classification: An End-to-End System
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Rabies epidemiology and control · Poxvirus research and outbreaks · Digital Media Forensic Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Computer Science and Engineering, “Ss. Cyril and Methodius University”, 1000 Skopje, North Macedonia
LOKA, 350 2nd Street, Suite 8, Los Altos, CA 94022, USA
Funding
Faculty of Computer Science and Engineering, University “Ss. Cyril and Methodius”, Skopje, Republic of North Macedonia
References (51)
Show source-provided references
1. Asif, S., Wenhui, Y., Jin, H., and Jinhai, S. (2020, January 11–14). Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Network. Proceedings of the 2020 IEEE 6th International Conference on Computer and Communications (ICCC), Chengdu, China.10.1109/ICCC51575.2020.9344870 ↗
2. Ibrahim, A.U., Ozsoz, M., Serte, S., Al-Turjman, F., and Yakoi, P.S. (2021). Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19. Cogn. Comput.10.1007/s12559-020-09787-5 ↗
3. Deep transfer learning based classification model for COVID-19 disease10.1016/j.irbm.2020.05.003 ↗
4. AI-based diagnosis of COVID-19 patients using X-ray scans with stochastic ensemble of CNNs10.1007/s13246-021-01060-9 ↗
5. Lung cancer classification using neural networks for CT images10.1016/j.cmpb.2013.10.011 ↗
6. Multiple instance learning for histopathological breast cancer image classification10.1016/j.eswa.2018.09.049 ↗
7. A survey on deep learning in medical image analysis10.1016/j.media.2017.07.005 ↗
8. AI in Medical Imaging Informatics: Current Challenges and Future Directions10.1109/JBHI.2020.2991043 ↗
9. Razzak, M.I., Naz, S., and Zaib, A. (2018). Classification in BioApps: Automation of Decision Making, Springer International Publishing.
10. AI-based detection of erythema migrans and disambiguation against other skin lesions10.1016/j.compbiomed.2020.103977 ↗
11. The skin cancer classification using deep convolutional neural network10.1007/s11042-018-5714-1 ↗
12. Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities10.1016/j.compbiomed.2020.104065 ↗
13. Skin Disease Recognition Method Based on Image Color and Texture Features10.1155/2018/8145713 ↗
14. Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). Proceedings of the Computer Vision—ECCV 2016, Springer International Publishing.10.1007/978-3-319-46454-1 ↗
15. Classification of Skin Disease using Ensemble Data Mining Techniques10.31557/APJCP.2019.20.6.1887 ↗
16. Srinivasu, P.N., SivaSai, J.G., Ijaz, M.F., Bhoi, A.K., Kim, W., and Kang, J.J. (2021). Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM. Sensors, 21.10.3390/s21082852 ↗
17. Global seroprevalence and sociodemographic characteristics of Borrelia burgdorferi sensu lato in human populations: A systematic review and meta-analysis10.1136/bmjgh-2021-007744 ↗
18. Powers, J., and McDowell, R.H. (2022, December 30). Insect Bites, Available online: https://www.ncbi.nlm.nih.gov/books/NBK537235/.
19. Insect bites10.1136/bmj.m2856 ↗
20. A review on Respiratory allergy caused by insects10.6026/97320630014540 ↗
21. Singstad, B.J. (2022, October 07). Mosquito or Ticks?. Available online: https://www.kaggle.com/bjoernjostein/tick-and-mosq.
22. Systems, A.T. (2022, December 30). Identifying Insect Bites—On cAInvas. Available online: https://medium.com/ai-techsystems/identifying-insect-bites-on-cainvas-96874143a48b.
23. Radja, B. (2022). A Healthcare System Using Deep Learning. [Master’s Thesis, Faculty of New Information and Communication Technologies, University of Kasdi Merbah Ouargla].
24. Yeung, E. (2022, October 11). bugbite_ID. Available online: https://github.com/eyyeung/bugbite_ID.
25. Innani, S., Dutande, P., Baheti, B., Baid, U., and Talbar, S. (2023). Deep Learning based Novel Cascaded Approach for Skin Lesion Analysis. arXiv.10.1007/978-3-031-31407-0_46 ↗
26. (2022, December 08). AutoML Vision Documentation. Available online: https://cloud.google.com/vision/automl/docs.
27. Chollet, F. (2017, January 21–26). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.195 ↗
28. Selenium Framework (2022, October 07). Available online: https://www.selenium.dev.
29. Methods of insect image capture and classification: A Systematic literature review10.1016/j.atech.2021.100023 ↗
30. Comert, C., Kulhandjian, M., Gul, O.M., Touazi, A., Ellement, C., Kantarci, B., and D’Amours, C. (2022, January 16–19). Analysis of Augmentation Methods for RF Fingerprinting under Impaired Channels. Proceedings of the 2022 ACM Workshop on Wireless Security and Machine Learning, San Antonio, TX, USA.10.1145/3522783.3529518 ↗
31. A new image classification method using CNN transfer learning and web data augmentation10.1016/j.eswa.2017.11.028 ↗
32. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20–25). Imagenet: A large-scale hierarchical image database, 2009. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, USA.10.1109/CVPR.2009.5206848 ↗
33. Automatic identification of Chagas disease vectors using data mining and deep learning techniques10.1016/j.ecoinf.2021.101270 ↗
34. Kumar, D., and Kukreja, V. (2021, January 5–7). N-CNN Based Transfer Learning Method for Classification of Powdery Mildew Wheat Disease. Proceedings of the 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India.10.1109/ESCI50559.2021.9396972 ↗
35. Montalbo, F.J.P., and Hernandez, A.A. (2020, January 28–29). An Optimized Classification Model for Coffea Liberica Disease using Deep Convolutional Neural Networks. Proceedings of the 2020 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), Langkawi, Malaysia.10.1109/CSPA48992.2020.9068683 ↗
36. An effective algorithm for hyperparameter optimization of neural networks10.1147/JRD.2017.2709578 ↗
37. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18–23). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00474 ↗
38. Chollet, F. (2022, December 30). Keras. Available online: https://keras.io.
39. A comparison of deep networks with ReLU activation function and linear spline-type methods10.1016/j.neunet.2018.11.005 ↗
40. Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv.
41. Very deep convolutional networks for large-scale image recognition Karen
42. Rawat, J., Logofătu, D., and Chiramel, S. (2020, January 5–7). Factors affecting accuracy of convolutional neural network using VGG-16. Proceedings of the International Conference on Engineering Applications of Neural Networks, Halkidiki, Greece.10.1007/978-3-030-48791-1_19 ↗
43. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7–12). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.10.1109/CVPR.2015.7298594 ↗
44. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27–30). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.308 ↗
45. Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21–26). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.243 ↗
46. Dense convolutional network and its application in medical image analysis
47. Ioffe, S., and Szegedy, C. (2015, January 6–11). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France.
48. An overview of classifier fusion methods
49. Zhou, Z.H. (2021). Machine Learning, Springer.10.1007/978-981-15-1967-3 ↗
50. Tensorflow lite: On-device machine learning framework
51. Zhang, C., Benz, P., Argaw, D.M., Lee, S., Kim, J., Rameau, F., Bazin, J.C., and Kweon, I.S. (2021, January 5–9). Resnet or densenet? Introducing dense shortcuts to resnet. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Online.10.1109/WACV48630.2021.00359 ↗