This paper presents the detection of brain tumors by using the VGG16 approach for grading from multiphase MRI images. It also depicts the comparative analysis among several outcomes coming from different baseline neural networks and deep learning configurations. Machine learning directly uses MRI images, with few sequential operations among multiphase MRIs. This paper illustrates the process that influences the potential of the deep learning machine. Neural networks generally involve the convolutional neural networks (CNN) for achieving the optimum enhancement on grading performance. Such processes also include visualization of kernels trained in several layers and visualize few self-learned features attained from CNN. Such research shows the deep learning approach with its applications in brain tumor segmentation. Researchers found difficulty in the automatic segmentation of brain tumors that provide great variability in sizes and shapes. Computed tomography (CT) and magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies, and treatment planning. The problems common to both CT and MR medical images are partial volume effect, different artifacts: example motion artifacts, ring artifacts, etc, and noise due to sensors and related electronic systems. In this paper, we propose an easy and unique segmentation process that provides competitive performance as well as speedy runtime for the evaluation of model performance in terms of loss and accuracy.