Analysis of Brain Tumor Using MRI Images

The increasing rates of deadly brain tumors in humans correspondingly increase the need for highly experienced medical personnel for diagnosis and treatment. Therefore, to reduce the workload and the time from suspicion of disease to diagnosis, then plan for suitable treatment, there is a need to automate the initial part of the process by implementing a Computer-Aided-Disease-Diagnosis (CADD) system for brain tumor classification. The aim of this research is to develop and deploy all the needed components to design and implement a working Computer-Aided-Disease-Diagnosis (CADD) system for brain tumor classification. First, by understanding the brain tumors themselves and the convention of their classification. Second, the convolution neural network (CNN) structure and functionality and how to manipulate it. Third, find different CNN architectures with promising results in similar tasks. Fourth, find and evaluate the applicability of available data sources. Fifth, implement the most promising solutions and explore the applicability of using transfer learning for the given task to take advantage of previously gained knowledge. Finally, evaluate the results of the experiments, where we show that the DenseNet121 architecture, either fully trained or using transfer learning, likely is the most appropriate candidate for the CADD system in development.