Automated toxicity test model based on a bio-inspired technique and AdaBoost classifier

Tharwat, A; Gaber, T; Hassanien, AE; Elhoseny, M

Abstract

Measuring toxicity is one of the most important steps to develop a new drug. During the drug development, animals are widely used to investigate the toxic effects by exposing them to the toxicants. Zebrafish embryo is one of the most suitable animals for testing toxicity of compounds due to (1) the transparency of zebrafish animals and (2) the production of a large number of embryos in each mating. However, due to the high number of embryos, manual inspection is not feasible enough, slow, and inaccurate. In this paper, using machine learning and bio-inspired techniques, a fully automated method was suggested to investigate the toxicity using microscope images of treated zebrafish embryos. In this method, firstly, the Segmentation-Based Fractal Texture Analysis (SFTA) technique was employed for extracting features from embryos’ images. Then, a new version of Grey Wolf Optimization (GWO) was proposed and applied to select the most discriminative features to increase the classification performance while reducing the required computational time for the classification process. Finally, the AdaBoost classifier was used to classify an unknown image to alive or coagulant (i.e. dead embryo due to its exposure to a toxic compound). The experimental results showed that the selected features using the proposed optimization algorithm achieved the highest accuracy reached to 99.47%, the maximum average reduction rate and lowest computational time. These promising results represent a good step towards using machine learning techniques along with the new version of GWO to develop a fully automated toxicity test using zebrafish embryos images.

Citation

Tharwat, A., Gaber, T., Hassanien, A., & Elhoseny, M. (2018). Automated toxicity test model based on a bio-inspired technique and AdaBoost classifier. Computers and Electrical Engineering, 71, 346-358. https://doi.org/10.1016/j.compeleceng.2018.07.049

• Journal Article Type: Article
• Acceptance Date: Jul 31, 2018
• Online Publication Date: Aug 9, 2018
• Publication Date: Oct 1, 2018
• Deposit Date: Sep 5, 2019
• Journal: Computers & Electrical Engineering
• Print ISSN: 0045-7906
• Electronic ISSN: 1879-0755
• Publisher: Elsevier
• Volume: 71
• Pages: 346-358
• DOI: https://doi.org/10.1016/j.compeleceng.2018.07.049
• Publisher URL: https://doi.org/10.1016/j.compeleceng.2018.07.049
• Related Public URLs: https://www.sciencedirect.com/journal/computers-and-electrical-engineering