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The five different machines in this study raised different issues; our aim is not to critique any particular system, but to draw out general findings across participants.
The design is safe, as it does not allow the patient to proceed with treatment, but some of the machines in this study are not effective in supporting the patient in solving the problem.
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We employ the extreme learning machine in this study to help obtain better initial points faster and avoid irrelevant time-consuming work, such as determining architecture and manual tuning.
Secondly, this discovery correlated with the scanner mode and application of the 16-slice spiral CT machine in this study.
For each machine in this study, we ran each test of 100 trials three times and took the average of the means and SDs.
For example, one machine in this study provided contextual information to the patient and walked the patient through step by step for many tasks.
(P13) No safety issues were identified for the machines involved in this study, when used as designed, for issues within the scope of what the technology can detect.
The most representative images showing the main pathological findings were selected from the database of the three US machines used in this study.
In Figure 3, this diagram has been determined for two machines in this case study.
The machine used in this study is a high level medical ultrasound machine.
Recent PET machines have a higher detection rate for small tumours than the machine used in this study, and coupled PET-CT appears to provide more accurate diagnoses than performing the two examinations separately (Cohade et al, 2003; Even-Sapir et al, 2004; Kamel et al, 2004; Nakamoto et al, 2007).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com