Culex and Aedes Aegypti Detection Model using Convolutional Neural Network and Image Processing Algorithm
Culex and Aedes Aegypti Detection Model using Convolutional Neural Network and Image Processing Algorithm |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-7 |
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| Year of Publication : 2025 | ||
| Author : Cherry R. Gumiran, Joel M. Gumiran | ||
| DOI : 10.14445/22315381/IJETT-V73I7P137 | ||
How to Cite?
Cherry R. Gumiran, Joel M. Gumiran, "Culex and Aedes Aegypti Detection Model using Convolutional Neural Network and Image Processing Algorithm," International Journal of Engineering Trends and Technology, vol. 73, no. 7, pp.484-492, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I7P137
Abstract
Dengue fever is an illness spread by mosquitoes. The disease became widespread worldwide as the number of reported cases increased year after year, especially in the Philippines, which had the highest number of infected patients in Asia in 2019. As a consequence, according to the press, many projects have been initiated and adopted. As a result, these were effectively addressed, but they should be followed religiously for long-term effect. As a result, automated technology has been integrated to identify the mosquito carrier's position effectively, referred to as mapping using AI, GIS, Fuzzy Logic, and CNN. However, images used in the previous studies are in static parts with no other species combined in them. Hence, images are practically clean and easy to segment and classify. IN contrast, these insects will mature and propagate. Thus, this paper proposes an automatic classification for living and moving Aedes and Culex, combined with other living organisms like different types of insects, dirt or soil, larvae, and different kinds of mosquitoes. This paper used six various pre-processing methodologies for cleaning the images, combined with the Convolutional Neural Network. There are seven layers used in CNN, which comprise Relu, Inception, max-pooling, and resizing. The generated model, images had reached 92.83 percent of validation accuracy rate, which thus proves the success rate of a CNN and the pre-processing.
Keywords
Aedes Aegypti, Culex, CNN, Image detection, Image processing.
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