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Position and Communication Papers of the 16th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 26

Medical Steel Fault Prediction Using Deep Learning Techniques

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DOI: http://dx.doi.org/10.15439/2021F2

Citation: Position and Communication Papers of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 26, pages 153–158 ()

Full text

Abstract. This research work focus on the assessment and evaluation of fault detection using deep learning techniques. The evaluation is made accordingly using Deep CNN with the variants corresponding to simple CNN, Resnet, Alexnet and Vgg\_16. Besides, classification accuracy is improved by parameter optimizing and sample size equalization strategy. Experimental results shows that evaluation using the proposed methods with Vgg\_16 gives an improved training accuracy of about 90\% and validation accuracy of about 87\%. This proves that fault detection and analysis in medical equipments and transplanting devices can be efficiently identified for better treatment and device management

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