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Mosquito Classification Using Convolutional Neural Network with Data Augmentation
Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh.
Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh.ORCID-id: 0000-0002-7473-8185
Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. (Pervasive and Mobile Computing)ORCID-id: 0000-0003-0244-3561
2021 (Engelska)Ingår i: Intelligent Computing and Optimization: Proceedings of the 3rd International Conference on Intelligent Computing and Optimization 2020 (ICO 2020) / [ed] Pandian Vasant, Ivan Zelinka, Gerhard-Wilhelm Weber, Springer Nature, 2021, s. 865-879Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Mosquitoes are responsible for the most number of deaths every year throughout the world. Bangladesh is also a big sufferer of this problem. Dengue, malaria, chikungunya, zika, yellow fever etc. are caused by dangerous mosquito bites. The main three types of mosquitoes which are found in Bangladesh are aedes, anopheles and culex. Their identification is crucial to take the necessary steps to kill them in an area. Hence, a convolutional neural network (CNN) model is developed so that the mosquitoes could be classified from their images. We prepared a local dataset consisting of 442 images, collected from various sources. An accuracy of 70% has been achieved by running the proposed CNN model on the collected dataset. However, after augmentation of this dataset which becomes 3,600 images, the accuracy increases to 93%. We also showed the comparison of some methods with the CNN method which are VGG-16, Random Forest, XGboost and SVM. Our proposed CNN method outperforms these methods in terms of the classification accuracy of the mosquitoes. Thus, this research forms an example of humanitarian technology, where data science can be used to support mosquito classification, enabling the treatment of various mosquito borne diseases.

Ort, förlag, år, upplaga, sidor
Springer Nature, 2021. s. 865-879
Serie
Advances in Intelligent Systems and Computing, ISSN 2194-5357, E-ISSN 2194-5365 ; 1324
Nyckelord [en]
Mosquito, Classication, Dengue, Malaria, Convolutional Neural Network, Data Augmentation
Nationell ämneskategori
Medieteknik
Forskningsämne
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Identifikatorer
URN: urn:nbn:se:ltu:diva-81688DOI: 10.1007/978-3-030-68154-8_74OAI: oai:DiVA.org:ltu-81688DiVA, id: diva2:1504618
Konferens
3rd International Conference on Intelligent Computing and Optimization (ICO 2020), Hua Hin, Thailand (Online), December 17-18, 2020
Anmärkning

ISBN för värdpublikation: 978-3-030-68153-1,  978-3-030-68154-8

Tillgänglig från: 2020-11-29 Skapad: 2020-11-29 Senast uppdaterad: 2021-10-25Bibliografiskt granskad

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