Automated Medical Visualization Application of Supervised Learning to Clinical Diagnosis, Disease and Therapy Management.docx


  • Adekunle Adeshina Faculty of Communication and Information Sciences
  • M.A. Dikko High Performance Computing Research Laboratory, Nigeria.


The rapid advancement and development in high performance computing, ultrafast computing, autonomous technologies and complexity of biomedical data for visualization and image guidance play a significant role in modern surgery to help surgeons perform their surgical procedures. Brain tumour diagnosis requires an enhanced, effective as well as accurate 3-D visualization system for navigation, reference, diagnosis as well as documentation. The automatic and effective 3-D high performance artificial intelligence-enabled medical visualization framework was designed and implemented using automated machine learning (AutoML) to take the advantage of complexity in the underlying datasets to help specialists in identifying the most appropriate regions of interest and their associated hyper parameters for optimizing performance, whilst simultaneously attempting to maximize the reliability of resulting predictions. C# and Compute Unified Device Architecture (CUDA) in Microsoft.NET environment in comparison side by side with visual basic studio was used for the implementation. The framework was evaluated for rendering processing speed with brain datasets obtained from the department of surgery, University of North Carolina, United States. Interestingly, our framework achieves 3-D visualization of the human brain, reliable enough to detect and locate possible brain tumor with high interactive speed and accuracy. 


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