Automatic Detection of Brain Tumour in MRI Images using Neural Network Classifier - R, Dr. Rajasree.; N, Patchiraja. (European Journal of Life Safety and Stability (2660-9630), 2022)
The brain is among the largest and most complex organs that control all the human body's metabolic activities. Abnormalities or injuries in the brain cause grievous problems and are sometimes lethal. Moreover, the development and metastasis of the tumor can be monitored and treated appropriately by MRI accurately. MRI can also render information on the surrounding tissues, necrotic tissues, and the active cancer tissue significant in tumor diagnosis for early therapy and continuous monitoring. Therefore, accurate segmentation of brain images is a very complicated and untiring task. Glioblastoma is antithetic to other brain tumors because of its penetrating and fuzzy structure. To identify and locate the glioblastoma exactly, the image segmentation requires details of borders and edges in specific. This regards multimodal image segmentation, and it demands deep learning approaches since there is more than one modal to be dealt with for an accurate prediction. This study strategizes multimodal semantic image segmentation to detect and diagnose brain tumors. Deep learning Multimodal Convolution Neural Network (MMCNN) with U-NET has been utilized in the proposed methodology to achieve effective image segmentation.