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Journal of Imaging, Volume 8, Issue 9

2022 September - 29 articles

Cover Story: Today, it is common to compare different tools for a certain task, such as segmentation or classification. What is the best way to compare them, however? This is especially important in the context of AI with many loss functions, optimisers and a plethora of architectures. This work presents a comparison framework, which is applied for the classification of Computed Tomography images of individuals with COVID-19, pneumonia or disease-free. Five architectures (ResNet-50, ResNet-50r, DenseNet-121, MobileNet-v3 and the state-of-the-art CaiT-24-XXS-224), Adam and AdamW optimisers, cross entropy and weighted cross entropy were combined to form 20 experiments with 10 repetitions and then bootstrapped for 1000 cycles. Performance was compared using the Friedman–Nemenyi non-parametric statistical test. View this paper
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J. Imaging - ISSN 2313-433X
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