Brain tumor segmentation using deep learning approach

The research under consideration studies the Brain tumor segmentation using deep learning approach. The goal of the research is to discover how the models' layer parameters and the hyper-parameters changes are the parameters that affect the models' accuracy and performance. This paper was motivated by the pressing need for medical imaging to be done with utmost accuracy, hence, the main goal of this research is to find the U-Net architecture design that will enable the brain tumor segmentation to be done with the highest possible accuracy. The systematic procedure was carried out, which consisted of the collection of a large number of brain tumor cases in an unbiased way. The models were firstly trained and then evaluated, on the other hand, the attention was mostly on the key performance indicators. The study's findings indicate the minor differences in model performance and accuracy which are most probably due to the various architectural sets. It is worth to mention that some of the configurations were more stable and accurate in segmentation, which shows that the architectural design is the base in the medical image processing tasks. The findings reveal how the architectural modifications that are made for the patients serve the purpose of the segmentation of the brain tumors. The research findings are giving the medical image segmentation practitioners and researchers an important direction and a new area for growth in the field.