Implementation and Analysis of SVR Based Machine Learning Approach for Real-Time Modelling of Tissue Deformation

摘要

Brain tumors are fatal diseases that are spread worldwide and affect all types of age groups. Due to its direct impact on Central Nervous System if tumor cells prevail at certain locations in the brain, the overall functionality of the body is disturbed and chances of a person approaching death accelerate. Tumors can be cancerous or non-cancerous but in many cases, the chances of complete recovery are less and as a result death rate has increased all over the world despite recent advancements in technology, equipment and awareness. So the main concern is to detect brain related diseases at early stages so that it does not spread into vital parts of brain and disrupt body functions. Also, more precise and accurate technologies are required to serve as aid in diagnosis, treatment and surgery of brain. Therefore, its high alarming time to monitor mortality statistics and develop faster and accurate methods to curb the situation by simulating tissue deformation and locating cancerous nodes which is a good area of research. A brain tumor is used to design the deformation model. Early stage detection of tumors is difficult from images. Moreover, the accuracy involved is less. Keeping all this into consideration, a machine learning approach has been developed to detect and model tissue deformation with classification of soft and hard tissues so that the tissues having risk of future problem can also be recognized. The machine learning is the approach which learns from the existing patterns and derives new patterns based on their input parameters. This approach can be used in real time for modeling of tissue deformation in image guided neurosurgery. The patient's deformation model can be designed and brain tumor patterns are given as input on the basis of which tumor in the brain is marked.