Automated zebrafish-based toxicity test using Bat optimization and AdaBoost classifier

Environmental exposure to toxicants is very important as it become part of our daily life. Therefore, it is necessary to investigate the toxic effects of chemical substances before consuming it. The best choice for performing this test is by applying the toxicants to different animals. The zebrafish embryos are the most suitable animal for this test. In this paper, a fully-automated system is proposed to classify zebrafish embryos to alive or coagulant (i.e. dead due to exposing to toxic compound). The embryos' images are used to extract some features using the Segmentation-based Fractal Texture Analysis (SFTA) technique. Moreover, Bat algorithm is used to select the most discriminative features and then AdaBoost classifier is used to match between testing and training features (i.e. to classify alive and coagulant embryos). The experiments have proved that choosing threshold value of SFTA technique and the selected features have a great impact on the classification accuracy. With classification rate around 98.15%, the experimental results have showed that the proposed model is a very promising step toward a fully-automated toxicity test using zebrafish embryos.

[1]  H O Villar,et al.  Toward the design of chemical libraries for mass screening biased against mutagenic compounds. , 2001, Journal of medicinal chemistry.

[2]  Pau-Choo Chung,et al.  A Fast Algorithm for Multilevel Thresholding , 2001, J. Inf. Sci. Eng..

[3]  Stephen H. Friend,et al.  Toxicogenomics and drug discovery: will new technologies help us produce better drugs? , 2002, Nature Reviews Drug Discovery.

[4]  Aboul Ella Hassanien,et al.  Towards an Automated Zebrafish-based Toxicity Test Model Using Machine Learning , 2015 .

[5]  Pablo Eliseo Reynoso Aguirre,et al.  Multi-Objective Optimization Using Bat Algorithm to Solve Multiprocessor Scheduling and Workload Allocation Problem , 2015 .

[6]  Markus Reischl,et al.  Robust identification of coagulated zebrafish eggs using image processing and classification techniques , 2009 .

[7]  Agma J. M. Traina,et al.  An Efficient Algorithm for Fractal Analysis of Textures , 2012, 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images.

[8]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[9]  Ali Kaveh,et al.  IMPROVED BAT ALGORITHM FOR OPTIMUM DESIGN OF LARGE-SCALE TRUSS STRUCTURES , 2015 .

[10]  H. Köhler,et al.  Developmental toxicity and stress protein responses in zebrafish embryos after exposure to diclofenac and its solvent, DMSO. , 2004, Chemosphere.

[11]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[12]  O. Hasançebi,et al.  Bat inspired algorithm for discrete size optimization of steel frames , 2014, Adv. Eng. Softw..

[13]  Xin-She Yang,et al.  Bat algorithm for multi-objective optimisation , 2011, Int. J. Bio Inspired Comput..

[14]  Chiranjib Chakraborty,et al.  Zebrafish: a complete animal model for in vivo drug discovery and development. , 2009, Current drug metabolism.

[15]  Václav Snásel,et al.  Plant Identification: Two Dimensional-Based Vs. One Dimensional-Based Feature Extraction Methods , 2015, SOCO.

[16]  Trevor Hastie,et al.  Multi-class AdaBoost ∗ , 2009 .

[17]  M. Kent,et al.  The State of the Art of the Zebrafish Model for Toxicology and Toxicologic Pathology Research—Advantages and Current Limitations , 2003, Toxicologic pathology.