A WeChat Application Combined with Convolutional Neural Network for Skin Cancer Recognition
- DOI
- 10.2991/978-94-6463-040-4_147How to use a DOI?
- Keywords
- WeChat application; Convolutional Neural Network; skin cancer recognition; HAM10000
- Abstract
The skin cancer detection process is complex and time-consuming. The rise of machine learning led to the invention of skin detection models. However, the models are rarely used in the real world and the few implementations are slow and not lightweight. This paper sought to solve the issue by developing a lightweight mobile application that runs diagnosis on the cloud, allowing users to assess their conditions preliminarily. Convolutional Neural Network as a powerful type of deep learning methods was considered to recognize the skin cancer in this study. Datasets collected from a large skin cancer dataset called HAM10000 was employed to verify the proposed method. Based on convolutional neural network, an application using WeChat platform was able run a skin-cancer detection model with 0.81 test accuracy in the cloud to diagnose a user’s image in under 20 seconds while maintaining the app’s size under two megabytes, achieving ease of access, lightweight, and speed.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Junlin Wu PY - 2022 DA - 2022/12/27 TI - A WeChat Application Combined with Convolutional Neural Network for Skin Cancer Recognition BT - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022) PB - Atlantis Press SP - 975 EP - 980 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-040-4_147 DO - 10.2991/978-94-6463-040-4_147 ID - Wu2022 ER -