Novel Deep Learning Architectures for Classification and Segmentation of Brain Tumors from MRI Images

Brain tumors pose a significant threat to human life, therefore it is very much necessary to detect them accurately in the early stages for better diagnosis and treatment. Brain tumors can be detected by the radiologist manually from the MRI scan images of the patients. However, the incidence of brain tumors has risen amongst children and adolescents in recent years, resulting in a substantial volume of data, as a result, it is time-consuming and difficult to detect manually. With the emergence of Artificial intelligence in the modern world and its vast application in the medical field, we can make an approach to the CAD (Computer Aided Diagnosis) system for the early detection of Brain tumors automatically. All the existing models for this task are not completely generalized and perform poorly on the validation data. So, we have proposed two novel Deep Learning Architectures - (a) SAETCN (Self-Attention Enhancement Tumor Classification Network) for the classification of different kinds of brain tumors. We have achieved an accuracy of 99.38% on the validation dataset making it one of the few Novel Deep learning-based architecture that is capable of detecting brain tumors accurately. We have trained the model on the dataset, which contains images of 3 types of tumors (glioma, meningioma, and pituitary tumors) and non-tumor cases. We have achieved an overall pixel accuracy of 99.23%. Introduction Brain Tumors are a huge concern in the field of medicine because of their high mortality rate. Brain tumor forms when there is an uncontrollable abnormal growth of the cells within the Brain. The abnormal growth may occur in the brain itself which is called a primary tumor or it may spread to the brain from the other parts of the body which are called secondary or metastatic tumors [8]. The proper reason and causes of brain tumors are not yet understood but according to researchers, they occur due to genetic mutations that affect cell growth and division [6]. This mutation can cause the cell to multiply causing the tumor.