Robust Automated detection of Nanocarriers’ Toxicity using Microscopic Image Analysis

Saini, B. and Srivastava, S. and Bajpai, A.K. (2019) Robust Automated detection of Nanocarriers’ Toxicity using Microscopic Image Analysis. EAI Endorsed Transactions on Scalable Information Systems, 6 (20): e6. ISSN 2032-9407

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Nanocarriers’ usage turned out to be a revolutionizing factor in the field of medical diagnosis and therapies. One of these therapies includes drug delivery system, where the nanocarriers are utilized for targeted and controlled delivery of drug to the diseased sites. Toxicity is one of the crucial issues which can arise during this process and needs to be addressed seriously and as early as possible. This paper reports an automated system for the relatively new and undiscovered area, where cell detection and evaluation play a major role in toxicity prediction of nanocarriers during the drug delivery process. The toxicity level was decided on the basis of dead cells count present in the microscopic images. The algorithm takes a few seconds to run and the overall accuracy of the proposed algorithm was found to be approx. 97% and 83% for different sets of images. The various image peculiarities which led to error include high cells clustering, poor contrast, and noisy background

Item Type: Article
Uncontrolled Keywords: Nanotechnology, Nanocarriers, Drug Delivery System, Toxicity, Image Processing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 08 Oct 2020 13:57
Last Modified: 08 Oct 2020 13:57

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