Apply Agglomerative Algorithm and Vgg16 on Brain Tumor Segmentation (Dataset to be Used Brats)

Medical imaging analysis relies heavily on the segmentation of brain tumours to help in treatment planning, diagnosis and patient monitoring. The recent breakthroughs in artificial intelligence and deep learning approaches have revealed promising outcomes in automating this crucial process. The BRATS (“Multimodal Brain Tumour Segmentation Challenge”) dataset is used throughout this study to investigate the potential of the agglomerative method in tandem with the VGG16 “convolutional neural network” architecture for brain tumor segmentation. Ground-truth tumor segmentation labels are provided with multimodal MRI scans from the BRATS dataset. These modalities include T1c, T1, FLAIR and T2. This research intends to improve clinical decision-making and patient care by increasing the precision and speed of brain tumor segmentation by the combined use of the agglomerative algorithm and VGG16.