Research Article
Convolution Neural Network-based Mosquito Classification System
@INPROCEEDINGS{10.4108/eai.24-3-2022.2318954, author={Ayesha Anam Irshad Siddiqui and Dr.Charansing Kayte}, title={Convolution Neural Network-based Mosquito Classification System}, proceedings={Proceedings of the 3rd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2022, 24-25 March 2022, New Delhi, India}, publisher={EAI}, proceedings_a={ICIDSSD}, year={2023}, month={5}, keywords={cnn mosquito aedes anopheles culex}, doi={10.4108/eai.24-3-2022.2318954} }- Ayesha Anam Irshad Siddiqui
Dr.Charansing Kayte
Year: 2023
Convolution Neural Network-based Mosquito Classification System
ICIDSSD
EAI
DOI: 10.4108/eai.24-3-2022.2318954
Abstract
Many diseases caused on by mosquito bites have spread all over the world in recent decades. The most prevalent diseases are malaria, dengue, and chikungunya. Worldwide, diseases transmitted by mosquitoes are indeed a threat. Due to the extreme significant degree of similarity in appearance between different mosquito species, classifying mosquito is a particularly tough task. We focused on deep learnign using convolutional neural network. Classifying mosquitoes is a particularly difficult pursuit because different mosquito species have an extremely high level of visual analogy. In this paper, we offer a convolutional neural network-based (CNN) technique for classifying mosquito species. We have built mosquito image classification models using CNN on our own mosquito dataset of 1800 mosquito images from three different genera Aedes, Anopheles, Culex. We made use of data augmentation. Results prior to and following augmentation were compared. After augmentation, we obtained accuracy of 84.51%.