Semantic Deep-Learning Approach for Medical Image Analytics
Gliomas are among the most aggressive primary tumours that occur in the brain and spinal cord. A glioma can affect your brain function and be life-threatening depending on its location and rate of growth. Gliomas are classified according to the type of glial cell involved in the tumour, as well as the tumour’s genetic features, which can help predict how the tumour will behave over time and the treatments most likely to work. Therefore, identifying the type of glioma will help determine appropriate treatment and prognosis. In this project, we propose to use data analytics to identify the type of glioma tumour using an elaborative mining process. The process requires data collection, data pre-processing and segmentation, application of deep learning and interpretation of the results. The innovative part of this project is in its ability to incorporate semantic elements in the learning process so that the results are reliable with high accuracy. This study will be extended to other types of medical images to identify other types of tumours and injuries.