TY - GEN
T1 - Mosquito larva classification method based on convolutional neural networks
AU - Sanchez-Ortiz, A.
AU - Fierro-Radilla, A.
AU - Arista-Jalife, A.
AU - Cedillo-Hernandez, M.
AU - Nakano-Miyatake, M.
AU - Robles-Camarillo, D.
AU - Cuatepotzo-Jiménez, V.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/4/3
Y1 - 2017/4/3
N2 - In Mexico a great number of diseases spread by the mosquitos Aedes has been reported. There are some regions on the country that this number is alarming. The spread of this disease becomes a public health problem and the government is worried about this situation and applied some methods for reducing the infection rate. One of principal methods relies on the localization of the mosquito's larvae and then fumigates them. The localization of Aedes larvae is accomplished through state programs which take a considerable time, making them not efficient enough. In this paper we propose a novel method based on convolutional neural networks, where a dataset of larva is used in training in order that the machine learns two types of mosquitos, genus Aedes and "others" genera. The digital images of larva are processed using a set of machine learning algorithms and as a result, the classification task is done. The proposed method would make the larva identification process more efficient, automatic and faster than the conventional methods, and thus the infection rates would be decrease. The results show a good performance on Aedes larva identification, proving that the system can be applied in the real world.
AB - In Mexico a great number of diseases spread by the mosquitos Aedes has been reported. There are some regions on the country that this number is alarming. The spread of this disease becomes a public health problem and the government is worried about this situation and applied some methods for reducing the infection rate. One of principal methods relies on the localization of the mosquito's larvae and then fumigates them. The localization of Aedes larvae is accomplished through state programs which take a considerable time, making them not efficient enough. In this paper we propose a novel method based on convolutional neural networks, where a dataset of larva is used in training in order that the machine learns two types of mosquitos, genus Aedes and "others" genera. The digital images of larva are processed using a set of machine learning algorithms and as a result, the classification task is done. The proposed method would make the larva identification process more efficient, automatic and faster than the conventional methods, and thus the infection rates would be decrease. The results show a good performance on Aedes larva identification, proving that the system can be applied in the real world.
KW - Aedes
KW - Classification
KW - Convolutional neural networks
KW - Larva
KW - Mosquito
UR - http://www.scopus.com/inward/record.url?scp=85018985455&partnerID=8YFLogxK
U2 - 10.1109/CONIELECOMP.2017.7891835
DO - 10.1109/CONIELECOMP.2017.7891835
M3 - Contribución a la conferencia
AN - SCOPUS:85018985455
T3 - 2017 International Conference on Electronics, Communications and Computers, CONIELECOMP 2017
BT - 2017 International Conference on Electronics, Communications and Computers, CONIELECOMP 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 27th International Conference on Electronics, Communications and Computers, CONIELECOMP 2017
Y2 - 22 February 2017 through 24 February 2017
ER -