Comparative Model Efficiency Analysis Based on Dissimilar Algorithms for Image Learning and Correction as a Means of Fault-Finding

Joe Benganga, Tshepo Kukuni, Ben Kotze () and Lepekola Lenkoe
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Joe Benganga: Department of Electrical, Electronic and Computer Systems Engineering, Central University of Technology, 20 President Brandt Street, Bloemfontein 9300, Free-State, South Africa
Tshepo Kukuni: Department of Electrical, Electronic and Computer Systems Engineering, Central University of Technology, 20 President Brandt Street, Bloemfontein 9300, Free-State, South Africa
Ben Kotze: Department of Electrical, Electronic and Computer Systems Engineering, Central University of Technology, 20 President Brandt Street, Bloemfontein 9300, Free-State, South Africa
Lepekola Lenkoe: Department of Electrical, Electronic and Computer Systems Engineering, Central University of Technology, 20 President Brandt Street, Bloemfontein 9300, Free-State, South Africa

Mathematics, 2025, vol. 13, issue 11, 1-13

Abstract: The introduction of technology in different sectors to optimise efficiency is increasing rapidly. As a result of the opportunities that artificial intelligence presents to different sectors by optimally performing tasks with less error compared to humans or traditional models, the use of AI in artefact detection is being investigated. This research paper thus presents a comparative model efficiency analysis based on dissimilar algorithms, namely CNN, VGG16, Inception_V3, and ResNet_50. The model developed was based on images that were obtained from a Toshiba CT scanner for two types of datasets (88 image datasets) and 170 image datasets, both comprising metal and ring artefacts. Furthermore, the results demonstrate higher data losses in the data transfer learning due to data recycling, suggesting that the model is prone to image feature losses when the model threshold is set at 75%. Additionally, two data transfer models were evaluated against “our model”. The results demonstrate that VGG16 performed better in terms of data accuracy than both the testing and training models, while the Resnet_50 algorithm performed poorly in terms of the loss encountered compared to the other three algorithms.

Keywords: artefact; CT scanner; metal artefact; ring artefact; machine learning; image learning; computer vision (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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