Deep learning framework to extract anatomy for mosquito image classification

Document Type : Computer Article

Authors

1 Department of Computer Engineering, Islamic Azad University, Yazd Branch, Yazd, Iran

2 Department of Computer Engineering, Meybod University, Meybod, Iran

3 Department of Computer Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran

Abstract

Mosquitoes are the main cause of the spread of dangerous diseases such as malaria, yellow fever, dengue fever, and Zika. The most effective way to control these diseases is to correctly identify the types of mosquito species. In the traditional method of identifying mosquitoes, identification is based on morphological diagnoses by specialized human beings with special skills. The most important classification challenge is to reduce the number of experts and the great diversity of different species of mosquitoes. In order to overcome this challenge, developing an automated method based on deep learning architectures to identify and classify mosquitoes will be a valuable resource for non-specialists.

This study proposes a convolutional network model that integrates the ResNet101 architecture and the Mask_RCNN technique to segment and classifies mosquito images. 2354 mosquito images of three species of Anopheles, Aedes, and Culex are compared with each other. In the proposed model, instead of entering the network as a complete image of a mosquito, first, the images are segmented, and then different parts of the abdomen, legs, wings, and head are given to the network as input. The corresponding binary mask of the described parts of the mosquito body is produced by the convolution network to extract the feature for each separate part and then calculate the loss value between the classified values and the image label. The evaluation results showed that the extraction of mosquito anatomy images affects the faster classification of images and the network performed better with 97.84% accuracy than normal.

Keywords

Main Subjects

References

[1] H.Caraballo and K.King,"Emergency department management of mosquito-borne illness: malaria, dengue, and West Nile virus", Emergency medicine practice,Vol .16, NO.5, May 2014 , pp.1-23.
[2] G.King Jonas," Developmental and comparative perspectives on mosquito immunity", Developmental& Comparative Immunology, Vol. 1o3, February 2020, pp.103458.
[3] G.Hopkins andR Freckleton," Declines in the numbers of amateur and professional taxonomists: implications for conservation", Animal conservation forum, Vol.5, NO. 3, August 2002, pp. 245-249.
[4] R.britz, A.hundsdörfer, U.fritz, " Funding, training, permits—the three big challenges of taxonomy", Megataxa, Vol.1, NO.1, January2020, pp.49-52.
[5] A. Arista-Jalife,M.Nakano, Z.Garcia-Nonoal, D.Robles-Camarillo, H.Perez-Meana and H.Arista-Viveros, "Aedes mosquito detection in its larval stage using deep neural networks", Knowledge-Based Systems, Vol.189,  February 2020, pp.104841.
[6] زهرا مروج؛ جواد آذرخش، "شبیه سازی و طبقه بندی وقایع کیفیت توان با استفاده از شبکه عصبی "، نشریه مدل سازی در مهندسی، دوره 13، شماره 41، تابستان 1394، صفحه 137-146.
[7] راضیه راستگو؛ کورش کیانی، "شناسایی چهره بااستفاده از تنطیم دقیق شبکه های کانولوشنی عمیق و رویکرد یادگیری انتقالی"، نشریه مدل سازی در مهندسی، دوره 17، شماره 58، پاییز 1398، صفحه 103-111.
[8] محمود معلم؛ علی اکبر پویان، "کشف ناهنجاری با استفاده از کد کننده خودکار مبتنی بر بلوک‌های LSTM"، نشریه مدل سازی در مهندسی ، دوره 17، شماره 56، بهار 1398، صفحه 191-211.
[9] A.Dhillon and GK.Verma," Convolutional neural network: a review of models, methodologies and applications to object detection", Progress in Artificial Intelligence, Vol.9 ,No.2 , Junuary 2020 , pp.85-112.
 [10] Dataset :http://www.mosquitoalert.com/en/mosquito-images-dataset/.
[11] M. Martineau, D. Conte, R.Raveaux, I.Arnault, D.Munier andG.Venturini," A survey on image-based insect classification", Pattern Recognition,  Vol.65 , May 2017 , pp.273-284.
[12] N. jaramilloo, JP. Dujardin, D.CalleLondoño andI.Fonseca-González, "Geometric morphometrics for the taxonomy of 11 species of Anopheles (N yssorhynchus) mosquitoes" , Medical and Veterinary Entomology,Vol.29 , NO.1 , March 2015,pp.26-36.
[13]AK.Banerjee, K.Kiran, US.Murty and C.Venkateswarlu, " Classification and identification of mosquito species using artificial neural networks", Computational Biology and Chemistry,Vol.32 ,NO.6 , December 2008, pp.442-447.
[14] KY.Lee, N.Chung Nand S.Hwang," Application of an artificial neural network (ANN) model for predicting mosquito abundances in urban areas", Ecological Informatics, Vol. 36, November2016 , pp.172-180.
[15] R.Wieland, A.Kerkow, L.Früh, H.Kampen andD.Walther, “Automated feature selection for a machine learning approach towardmodelinga mosquito distribution", Ecological Modelling, Vol.352, May 2017 pp.108-112.
[16] Y.Li, D.Zilli, H.Chan, I.Kiskin, M.Sinka, S.Robertsand K.Willis,"Mosquito detection with low-cost smartphones: data acquisition for malaria research",arXiv preprint arXiv:1711.06346,November2017.
[17] L.Früh L, H.Kampen, A.Kerkow,GA.Schaub, D.Walther andR.Wieland, "Modelling the potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations", Ecological Modelling, NO.388 , November 2018,pp.136-144.
[18] M. Minakshi, P.Bharti, T.Bhuiyan, S.Karie, and S.Chellappan, " A framework based on deep neural networks to extract anatomy of mosquitoes from images", Scientific Reports, Vol.10 , No.1 , August 2020 ,pp.1-10.
[19] پانیذ تیموری؛ مهدی مزینانی؛ راحیل حسینی، "ارایه یک مدل هوشمند قطعه‌بندی مبتنی بر منطق فازی و تبدیل موجک گسسته در تصاویر دیجیتالی جهت شناسایی سرطان معده"، نشریه مدل سازی در مهندسی، دوره 18، شماره 6، زمستان1399، صفحه 131-150.
[20] J.Park, DI.Kim, B.Choi, W.Kang and HW.Kwon," Classification and morphological analysis of vector mosquitoes using deep convolutional neural networks" ,Scientific reports, Vol.10 , No.1 ,January2020, pp.1-12.
[21] RF.Simões, AB.Wilke, CR.Chagas, RM.Menezes, L.Suesdek, LC.Multini, FS.Silva, MG.Grech,MT.Marrelli andK.Kirchgatter, "Wing geometric morphometricsas a tool for the Identification of culex subgenus mosquitoes of culex (Diptera: Culicidae)", Insects, Vol.11 , NO. 9, September2020,pp.567-580.
[22] E.Gokcay,"An information-theoretic instance-based classifier".Information Sciences,Vol.536, October  2020, pp.263-276.
[23]L.Früh, H.Kampen, A.Kerkow, GA.Schaub, D.Walther and R.Wieland, "Modellingthe potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations", Ecological Modelling, Vol. 388,  November 2018, pp.136-144.
[24] T.Kasinathan and SR.Uyyala, " Machine learning ensemble with image processing for pest identification and classification in field crops", Neural Computing and Applications, Vol.33 , No. 13,July 2021,pp.7491-7504.
[25] Hasan, Hayder, Helmi ZM Shafri, and Mohammed Habshi. "A comparison between support vector machine (SVM) and convolutional neural network (CNN) models for hyperspectral image classification." In IOP Conference Series: Earth and Environmental Science, VOl. 357, NO. 1 , November 2019, pp. 012035.
[26] E.Fanioudakis, M.Geismar andI.Potamitis, "Mosquito wingbeat analysis and classification using deep learning" ,26th European Signal Processing Conference (EUSIPCO) , Rome,Italy ,September2018, pp. 2410-2414.
[27] Y.Guo, Y.Liu, T.Georgiou and MS.Lew, "A review of semantic segmentation using deep neural networks", International journal of multimedia information retrieval, Vol. 7, No. 2, Junuary 2018 , pp.87-93.
[28] HD.Cheng, XH.Jiang,Y. Sun and J.Wang, " Color image segmentation: advances and prospects", Pattern recognition, Vol.34 , NO.12, December 2001 , pp.2259-2281.
[29] Z. Huang, X.Wang, J.Wang, W.Liu and J.Wang, "Weakly-supervised semantic segmentation network with deep seeded region growing", Proceedings of the IEEE conference on computer vision and pattern recognition, June 2018 , USA, pp. 7014-7023.
[30] TD.Júnior, R.Rieder, JR.Domênico andD. Lau, " InsectCV: A system for insect detection in the lab from trap images", Ecological Informatics, Vol.67, March2022, pp.101516.
[32]Khalighifar, Ali, Daniel Jiménez-García, Lindsay P. Campbell, KoffiMensahAhadji-Dabla, Fred Aboagye-Antwi, Luis Arturo Ibarra-Juárez, and A. Townsend Peterson. "Application of DeepLearning to Community-Science-Based Mosquito Monitoring and Detection of Novel Species."Journal of medical entomology, Vol.59, No.1, January 2022, PP. 355-362.
[33]Xu X, Zhao M, Shi P, Ren R, He X, Wei X, Yang H. "Crack Detection and Comparison Study Based on Faster R-CNN and Mask R-CNN", Sensors ,Vol.22, No.3,February 2022, p1215.
[34]Ulku, Irem, and ErdemAkagündüz. "A survey on deep learning-based architectures for semantic segmentation on 2d images."Applied Artificial Intelligence, February 2022, PP. 1-45.
[35]Cao, Xingmei, Jeng-Shyang Pan, Zhengdi Wang, Zhonghai Sun, Anwar ulHaq, Wenyu Deng, and Shuangyuan Yang. "Application of generated mask method based on Mask R-CNN in classification and detection of melanoma", Computer Methods and Programs in Biomedicine,Vol. 207 , August 2021,PP 106174.
[36] BP.Amiruddin andRE.Kadir, " CNN architectures performance evaluation for image classification of mosquito inIndonesia",  International Seminar on Intelligent Technology and Its Applications (ISITIA), July 2020,  Surabaya, Indonesia, pp. 223-227.
[37] V.Kittichai, T.Pengsakul, K.Chumchuen, Y.Samung, P.Sriwichai, N.Phatthamolrat, T.Tongloy, K.Jaksukam, S.Chuwongin, and S.Boonsang, " Deep learning approaches for challenging species and gender identification of mosquito vectors", Scientific reports, Vol.11 , NO.1 ,March 2021 , pp.1-4.
[38] Marques, Alan Caio R., Marcos M. Raimundo, Ellen Marianne B. Cavalheiro, Luis FP Salles, Christiano Lyra, and Fernando J. Von Zuben. "Ant genera identification using an ensemble of convolutional neural networks." Plos one, Vol. 13, NO. 1, january2018, pp e0192011.
[39] Motta, Daniel, Alex Álisson Bandeira Santos, Ingrid Winkler, Bruna Aparecida Souza Machado, Daniel André Dias Imperial Pereira, Alexandre Morais Cavalcanti, Eduardo Oyama Lins Fonseca, Frank Kirchner, and Roberto Badaró. "Application of convolutional neural networks for classification of adult mosquitoes in the field." PloS one, Vol.14, No. 1, January 2019, pp e0210829.
Journal of Modeling in Engineering
Volume 20, Issue 70
September 2022
Pages 107-120

Files

History

  • Receive Date: 14 February 2022
  • Revise Date: 23 April 2022
  • Accept Date: 08 May 2022

Share

How to cite

Statistics