. 2022 Jan;29(1):583-594.
doi: 10.1016/j.sjbs.2021.09.021.
Epub 2021 Sep 20.
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Saudi J Biol Sci.
2022 Jan.
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Abstract
Every year about one million people die due to diseases transmitted by mosquitoes. The infection is transmitted to a person when an infected mosquito stings, injecting the saliva into the human body. The best possible way to prevent a mosquito-borne infection till date is to save the humans from exposure to mosquito bites. This study proposes a Machine Learning (ML) and Deep Learning based system to detect the presence of two critical disease spreading classes of mosquitoes such as the Aedes and Culex. The proposed system will effectively aid in epidemiology to design evidence-based policies and decisions by analyzing the risks and transmission. The study proposes an effective methodology for the classification of mosquitoes using ML and CNN models. The novel RIFS has been introduced which integrates two types of feature selection techniques – the ROI-based image filtering and the wrappers-based FFS technique. Comparative analysis of various ML and deep learning models has been performed to determine the most appropriate model applicable based on their performance metrics as well as computational needs. Results prove that ETC outperformed among the all applied ML model by providing 0.992 accuracy while VVG16 has outperformed other CNN models by giving 0.986 of accuracy.
Keywords:
CNN; Disease epidemiology; Image classification; ML; RIFS; ROI; Vector mosquito.
© 2021 The Author(s).
Conflict of interest statement
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Figures
Sample of dataset.
Fig. 1
Sharp filter.
Fig. 2
Fig. 3
Sample image before and after applying sharp filter.
Fig. 4
Identity mapping function in ResNet-50.
RIFS Workflow diagram.
Fig. 5
RoIs from an image.
Fig. 6
Fig. 7
Proposed methodology diagram.
Fig. 8
Training and testing accuracy loss for CNNs with RIFS approach.
Fig. 9
Accuracy comparison between all approaches.
Fig. 10
Study (Akter et al., 2020) accuracy and loss.
Fig. 11
Study (Park et al., 2020) accuracy and loss.
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