Zhiyong John Yang1, Dech Dokpuang 2, Rinki Murphy 3, Reza Nemati 4, Xavier Yin 5, Kevin Haokun He 5, and Jun Lu1
1School of Biomedical Science, Auckland University of Technology, Auckland, New Zealand, 2Auckland University of Technology, Auckland, New Zealand, 3University of Auckland, Auckland, New Zealand, 4. Canterbury Health Laboratories, Christchurch, New Zealand, 5Saint Kentigern College, Auckland, New Zealand
1School of Biomedical Science, Auckland University of Technology, Auckland, New Zealand, 2Auckland University of Technology, Auckland, New Zealand, 3University of Auckland, Auckland, New Zealand, 4. Canterbury Health Laboratories, Christchurch, New Zealand, 5Saint Kentigern College, Auckland, New Zealand
It now can become a
faster way using AI to recognize the pancreatic fat changes and correlate them
to metabolic disorders. This also provides a possibility that prognose any
latency diseases via software or online in the near future.
Fig.3 Auto segmentation results are shown in a. original water image; b.
auto segmentation results; c. artificial segmentation results, respectively.
Loss values, accuracy rate, rr values are shown in the line chart
Fig.2 Image pre-processing framework
