Optimization of convolutional neural network hyperparameters for automatic classification of adult mosquitoes
| Autor: | Matias Valdenegro-Toro, Bruna Aparecida Souza Machado, Otavio Gonçalvez Vicente Ribeiro-Filho, Alex Álisson Bandeira Santos, Roberto Badaró, Luis Octavio Arriaga Camargo, Frank Kirchner, Daniel Motta |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Male Computer science Entropy Overfitting Disease Vectors medicine.disease_cause computer.software_genre Convolutional neural network Mosquitoes Dengue fever Dengue 0302 clinical medicine Aedes Medicine and Health Sciences Image Processing Computer-Assisted Chikungunya Multidisciplinary biology Artificial neural network Zika Virus Infection Physics Eukaryota Insects Culex Infectious Diseases Physical Sciences Viruses Medicine Thermodynamics Female Chikungunya virus Research Article Computer and Information Sciences Aedes albopictus Arthropoda Neural Networks Infectious Disease Control Science 030231 tropical medicine Aedes aegypti Mosquito Vectors Aedes Aegypti Machine learning 03 medical and health sciences parasitic diseases medicine Animals Automation Laboratory business.industry fungi Organisms Biology and Life Sciences Zika Virus Dengue Virus medicine.disease biology.organism_classification Invertebrates Culex quinquefasciatus Insect Vectors Species Interactions 030104 developmental biology Culicidae Chikungunya Fever Artificial intelligence business computer Zoology Entomology Arboviruses Neuroscience |
| Zdroj: | PLoS ONE PLoS ONE, Vol 15, Iss 7, p e0234959 (2020) |
| ISSN: | 1932-6203 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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