Segmentation of brain tumors is a daunting process comprising the delineation of heterogeneous cancerous tissues and diffuse types in anatomical representations of the brain. Deep learning techniques have recently made important strides in the segmentation of brain tumors. However, owing to the irregularity of the tumor, most of the deep learning-based segmentation techniques are not used directly for tumor detection. Although recent studies are capable of addressing the irregularity issue and retaining permutation invariance, many approaches struggle to catch the valuable high-dimensional local features of finer resolution. Inspired by the fuzzy learning methods and an analysis of the shortcomings of existing methods, an automated fuzzy neighborhood learning-based 3D segmentation technique has been proposed for the detection of cerebrum tumors in 3D images. In this technique, the fuzzy neighborhood function is deeply integrated with the proposed network architecture. This technique has been evaluated on BRATS 2013dataset. The simulation results show that the proposed brain tumor detection technique is superior to other methods in the diagnosis of brain tumors with the dice coefficient of 0.85 and the Jaccard index of 0.74.