Transfer Learning for Mosquito Classification Using VGG16

DOI

10.2991/978-94-6463-196-8_36How to use a DOI?

Keywords

VGG16; CNN; Mosquito; Transfer Learning; MSCMosquito Species Classification

Abstract

A challenge in computer vision known mosquito classification hasn't gained much traction. Automatic mosquito species credentials using real-time images is a crucial feature. Mosquitoes are a serious matter of concern since they can spread diseases including dengue fever, zika, and malaria. It's important to control mosquito populations in order to effectively control mosquitoes. The World Health Organization reported that over a million people worldwide experience malaria and dengue fever each year. In this investigation, we analyze a deep learning vgg-16 network architecture for mosquito specifically chosen. On our mosquito dataset, which included six (6) species of mosquito. The pre-trained vgg-16 network architecture with transfer learning technique was studied and proved to identify distinct mosquito species, with an average accuracy rate of 97.1751 percent Loss 0.094359393954277. The results of VGG 16 and CNN are compared. The results show that CNN with multi class classifier is achieving 85.75 percent accuracy and VGG 16 with 97.1751 accuracy. It shows that the VGG 16 model is pretty good in results as compare to CNN.

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.

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TY  - CONF
AU  - Ayesha Anam Siddiqui
AU  - Charansing Kayte
PY  - 2023
DA  - 2023/08/10
TI  - Transfer Learning for Mosquito Classification Using VGG16
BT  - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
PB  - Atlantis Press
SP  - 471
EP  - 484
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-196-8_36
DO  - 10.2991/978-94-6463-196-8_36
ID  - Siddiqui2023
ER  -