Accurate Brain Tumor Segmentation and Detection using Multi-Task Learning with GlobalNet and FusionNet

Accurate and quick brain tumor diagnosis and treatment are critical for the successful management of these disorders, which can have a substantial influence on an individual’s quality of life and even be deadly. This work describes a novel technique for brain tumor segmentation and classification that employs three deep learning architectures: GlobalNet, Multi-task Learning, and FusionNet. GlobalNet is a convolutional neural network that incorporates global context information into the model via a global pooling layer, whereas Multi-task Learning allows the model to do numerous tasks concurrently, enhancing each task’s performance by using shared knowledge among them. FusionNet is a deep fully residual convolutional neural network that combines information extracted from multiple CNNs trained on various medical imaging modalities to improve the accuracy and resilience of brain tumor segmentation. The proposed method is shown to be robust and accurate in segmenting and categorizing brain tumors, providing an effective solution with better accuracy in predicting the brain tumors and segmenting the affected part.