Optimization of convolutional neural network hyperparameters for automatic classification of adult mosquitoes
The economic and social impacts due to diseases transmitted by mosquitoes in the latest years have been significant. Currently, no specific treatment or commercial vaccine exists for the control and prevention of arboviruses, thereby making entomological characterization fundamental in combating dis...
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| creator | Motta, Daniel Santos, Alex Alisson Bandeira Machado, Bruna Aparecida Souza Ribeiro-Filho, Otavio Goncalvez Vicente Camargo, Luis Octavio Arriaga Valdenegro-Toro, Matias Alejandro Kirchner, Frank Badaro, Roberto |
| description | The economic and social impacts due to diseases transmitted by mosquitoes in the latest years have been significant. Currently, no specific treatment or commercial vaccine exists for the control and prevention of arboviruses, thereby making entomological characterization fundamental in combating diseases such as dengue, chikungunya, and Zika. The morphological identification of mosquitos includes a visual exam of the samples. It is time consuming and requires adequately trained professionals. Accordingly, the development of a new automated method for realizing mosquito-perception and -classification is becoming increasingly essential. Therefore, in this study, a computational model based on a convolutional neural network (CNN) was developed to extract features from the images of mosquitoes and then classify the species Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus. In addition, the model was trained to detect the mosquitoes of the genus Aedes. To train CNNs to perform the automatic morphological classification of mosquitoes, a dataset, which included 7,561 images of the target mosquitoes and 1,187 images of other insects, was acquired. Various neural networks, such as Xception and DenseNet, were used for developing the automatic-classification model based on images. A structured optimization process of random search and grid search was developed to select the hyperparameters set and increase the accuracy of the model. In addition, strategies to eliminate overfitting were implemented to increase the generalization of the model. The optimized model, during the test phase, obtained the balanced accuracy (BA) of 93.5% in classifying the target mosquitoes and other insects and the BA of 97.3% in detecting the mosquitoes of the genus Aedes in comparison to Culex. The results provide fundamental information for performing the automatic morphological classification of mosquito species. Using a CNN-embedded entomological tool is a valuable and accessible resource for health workers and non-taxonomists for identifying insects that can transmit infectious diseases. |
| doi_str_mv | 10.1371/journal.pone.0234959 |
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Currently, no specific treatment or commercial vaccine exists for the control and prevention of arboviruses, thereby making entomological characterization fundamental in combating diseases such as dengue, chikungunya, and Zika. The morphological identification of mosquitos includes a visual exam of the samples. It is time consuming and requires adequately trained professionals. Accordingly, the development of a new automated method for realizing mosquito-perception and -classification is becoming increasingly essential. Therefore, in this study, a computational model based on a convolutional neural network (CNN) was developed to extract features from the images of mosquitoes and then classify the species Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus. In addition, the model was trained to detect the mosquitoes of the genus Aedes. To train CNNs to perform the automatic morphological classification of mosquitoes, a dataset, which included 7,561 images of the target mosquitoes and 1,187 images of other insects, was acquired. Various neural networks, such as Xception and DenseNet, were used for developing the automatic-classification model based on images. A structured optimization process of random search and grid search was developed to select the hyperparameters set and increase the accuracy of the model. In addition, strategies to eliminate overfitting were implemented to increase the generalization of the model. The optimized model, during the test phase, obtained the balanced accuracy (BA) of 93.5% in classifying the target mosquitoes and other insects and the BA of 97.3% in detecting the mosquitoes of the genus Aedes in comparison to Culex. The results provide fundamental information for performing the automatic morphological classification of mosquito species. Using a CNN-embedded entomological tool is a valuable and accessible resource for health workers and non-taxonomists for identifying insects that can transmit infectious diseases.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0234959</identifier><identifier>PMID: 32663230</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Aedes ; Arbovirus diseases ; Artificial intelligence ; Artificial neural networks ; Automatic classification ; Automation ; Biology and Life Sciences ; Classification ; Computer and Information Sciences ; Computer applications ; Control ; Culicidae ; Dengue ; Dengue fever ; Disease transmission ; Feature extraction ; Identification and classification ; Image acquisition ; Image classification ; Infectious diseases ; Insect carriers of disease ; Insects ; Medical personnel ; Medicine and Health Sciences ; Methods ; Model accuracy ; Model testing ; Mosquitoes ; Neural networks ; Optimization ; Physical Sciences ; Species classification ; Target recognition ; Vaccines ; Vector-borne diseases ; Workers (insect caste) ; Zika virus</subject><ispartof>PloS one, 2020-07, Vol.15 (7), p.e0234959-e0234959</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Motta et al. 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Currently, no specific treatment or commercial vaccine exists for the control and prevention of arboviruses, thereby making entomological characterization fundamental in combating diseases such as dengue, chikungunya, and Zika. The morphological identification of mosquitos includes a visual exam of the samples. It is time consuming and requires adequately trained professionals. Accordingly, the development of a new automated method for realizing mosquito-perception and -classification is becoming increasingly essential. Therefore, in this study, a computational model based on a convolutional neural network (CNN) was developed to extract features from the images of mosquitoes and then classify the species Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus. In addition, the model was trained to detect the mosquitoes of the genus Aedes. To train CNNs to perform the automatic morphological classification of mosquitoes, a dataset, which included 7,561 images of the target mosquitoes and 1,187 images of other insects, was acquired. Various neural networks, such as Xception and DenseNet, were used for developing the automatic-classification model based on images. A structured optimization process of random search and grid search was developed to select the hyperparameters set and increase the accuracy of the model. In addition, strategies to eliminate overfitting were implemented to increase the generalization of the model. The optimized model, during the test phase, obtained the balanced accuracy (BA) of 93.5% in classifying the target mosquitoes and other insects and the BA of 97.3% in detecting the mosquitoes of the genus Aedes in comparison to Culex. The results provide fundamental information for performing the automatic morphological classification of mosquito species. Using a CNN-embedded entomological tool is a valuable and accessible resource for health workers and non-taxonomists for identifying insects that can transmit infectious diseases.</description><subject>Aedes</subject><subject>Arbovirus diseases</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Automatic classification</subject><subject>Automation</subject><subject>Biology and Life Sciences</subject><subject>Classification</subject><subject>Computer and Information Sciences</subject><subject>Computer applications</subject><subject>Control</subject><subject>Culicidae</subject><subject>Dengue</subject><subject>Dengue fever</subject><subject>Disease transmission</subject><subject>Feature extraction</subject><subject>Identification and classification</subject><subject>Image acquisition</subject><subject>Image classification</subject><subject>Infectious diseases</subject><subject>Insect carriers of 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of convolutional neural network hyperparameters for automatic classification of adult mosquitoes</title><author>Motta, Daniel ; Santos, Alex Alisson Bandeira ; Machado, Bruna Aparecida Souza ; Ribeiro-Filho, Otavio Goncalvez Vicente ; Camargo, Luis Octavio Arriaga ; Valdenegro-Toro, Matias Alejandro ; Kirchner, Frank ; Badaro, Roberto</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c669t-20cd57f0829bd2f7353347ee872a5d7e955a05020d793100a17716f0fb13398e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Aedes</topic><topic>Arbovirus diseases</topic><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Automatic classification</topic><topic>Automation</topic><topic>Biology and Life Sciences</topic><topic>Classification</topic><topic>Computer and Information Sciences</topic><topic>Computer 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Souza</au><au>Ribeiro-Filho, Otavio Goncalvez Vicente</au><au>Camargo, Luis Octavio Arriaga</au><au>Valdenegro-Toro, Matias Alejandro</au><au>Kirchner, Frank</au><au>Badaro, Roberto</au><au>Zhang, Jie</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimization of convolutional neural network hyperparameters for automatic classification of adult mosquitoes</atitle><jtitle>PloS one</jtitle><date>2020-07-14</date><risdate>2020</risdate><volume>15</volume><issue>7</issue><spage>e0234959</spage><epage>e0234959</epage><pages>e0234959-e0234959</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The economic and social impacts due to diseases transmitted by mosquitoes in the latest years have been significant. Currently, no specific treatment or commercial vaccine exists for the control and prevention of arboviruses, thereby making entomological characterization fundamental in combating diseases such as dengue, chikungunya, and Zika. The morphological identification of mosquitos includes a visual exam of the samples. It is time consuming and requires adequately trained professionals. Accordingly, the development of a new automated method for realizing mosquito-perception and -classification is becoming increasingly essential. Therefore, in this study, a computational model based on a convolutional neural network (CNN) was developed to extract features from the images of mosquitoes and then classify the species Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus. In addition, the model was trained to detect the mosquitoes of the genus Aedes. To train CNNs to perform the automatic morphological classification of mosquitoes, a dataset, which included 7,561 images of the target mosquitoes and 1,187 images of other insects, was acquired. Various neural networks, such as Xception and DenseNet, were used for developing the automatic-classification model based on images. A structured optimization process of random search and grid search was developed to select the hyperparameters set and increase the accuracy of the model. In addition, strategies to eliminate overfitting were implemented to increase the generalization of the model. The optimized model, during the test phase, obtained the balanced accuracy (BA) of 93.5% in classifying the target mosquitoes and other insects and the BA of 97.3% in detecting the mosquitoes of the genus Aedes in comparison to Culex. The results provide fundamental information for performing the automatic morphological classification of mosquito species. Using a CNN-embedded entomological tool is a valuable and accessible resource for health workers and non-taxonomists for identifying insects that can transmit infectious diseases.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>32663230</pmid><doi>10.1371/journal.pone.0234959</doi><tpages>e0234959</tpages><orcidid>https://orcid.org/0000-0003-1655-0325</orcidid><oa>free_for_read</oa></addata></record> |
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| source | DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Aedes Arbovirus diseases Artificial intelligence Artificial neural networks Automatic classification Automation Biology and Life Sciences Classification Computer and Information Sciences Computer applications Control Culicidae Dengue Dengue fever Disease transmission Feature extraction Identification and classification Image acquisition Image classification Infectious diseases Insect carriers of disease Insects Medical personnel Medicine and Health Sciences Methods Model accuracy Model testing Mosquitoes Neural networks Optimization Physical Sciences Species classification Target recognition Vaccines Vector-borne diseases Workers (insect caste) Zika virus |
| title | Optimization of convolutional neural network hyperparameters for automatic classification of adult mosquitoes |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T12%3A02%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Optimization%20of%20convolutional%20neural%20network%20hyperparameters%20for%20automatic%20classification%20of%20adult%20mosquitoes&rft.jtitle=PloS%20one&rft.au=Motta,%20Daniel&rft.date=2020-07-14&rft.volume=15&rft.issue=7&rft.spage=e0234959&rft.epage=e0234959&rft.pages=e0234959-e0234959&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0234959&rft_dat=%3Cgale_plos_%3EA629444497%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2423782988&rft_id=info:pmid/32663230&rft_galeid=A629444497&rft_doaj_id=oai_doaj_org_article_9583a589e8f3493885ad3fe73044dfe1&rfr_iscdi=true |