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

Tharwat, A, Gaber, T ORCID:, Hassanien, AE and Elhoseny, M 2018, 'Automated toxicity test model based on a bio-inspired technique and AdaBoost classifier' , Computers & Electrical Engineering, 71 , pp. 346-358.

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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.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Computers & Electrical Engineering
Publisher: Elsevier
ISSN: 0045-7906
Related URLs:
Depositing User: Dr. Tarek Gaber
Date Deposited: 05 Sep 2019 12:47
Last Modified: 16 Feb 2022 02:31

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