A Deep Transfer Learning Framework for the Multi-Class Classification of Vector Mosquito Species

• Autorzy: Pise Reshma , Patil Kailas

Identyfikatory

DOI

10.12911/22998993/168501

• Języki publikacji: EN

Abstrakty

EN

Mosquito borne diseases pose a substantial threat to public health. Vector surveillance and vector control approaches are critical to diminish the mosquito population. Quick and precise identification of mosquito species predominant in a geographic area is essential for ecological monitoring and devise effective vector control strategies in the targeted areas. There has been a growing interest in fine tuning the pretrained deep convolutional neural network models for the vision based identification of insect genera, species and gender. Transfer learning is a technique commonly applied to adapt a pre-trained model for a specific task on a different dataset especially when the new dataset has limited number of training images. In this research work, we investigate the capability of deep transfer learning to solve the multi-class classification problem of mosquito species identification. We train the pretrained deep convolutional neural networks in two transfer learning approaches: (i) Feature Extraction and (ii) Fine-tuning. Three state-of-the-art pretrained models including VGG-16, ResNet-50 and GoogLeNet were trained on a dataset of mobile captured images of three vector mosquito species: Aedes Aegypti , Anopheles Stephensi and Culex Quinquefasciatus. The results of the experiments show that GoogLeNet outperformed the other two models by achieving classification accuracy of 92.5% in feature extraction transfer learning and 96% with fine-tuning. Also, it was observed that fine-tuning the pretrained models improved the classification accuracy.

Słowa kluczowe

EN

computer vision; convolutional neural network; mosquito classification; deep transfer learning; vector control

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2023
• Tom: Vol. 24, nr 9
• Strony: 183--191
• Opis fizyczny: Bibliogr. 38 poz., rys., tab.

Twórcy

autor

Pise Reshma

• Department of Computer Engg., Vishwakarma University, Survey No. 2, 3, 4, Laxmi Nagar, Kondhwa Budruk,Pune, 411 048, Maharashtra, India

• https://orcid.org/0000-0001-7511-3446

autor

Patil Kailas

• Department of Computer Engg., Vishwakarma University, Survey No. 2, 3, 4, Laxmi Nagar, Kondhwa Budruk,Pune, 411 048, Maharashtra, India

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