Brain Tumor Segmentation and Detection using Deep Learning Method based on ResNet152

A brain tumor should be segmented and detected because through this proper management, treatment planning could allow for accurate diagnosis. This is the root cause of all evils in organ segmentation using traditional solutions that are fast but lack accuracy needed for a clinical problem. In this study, we propose a deep learning method to enhance the segmentation accuracy by adoption of ResNet152. Our proposed model works better when compared to the interacting methods such as Multimodal CNN, U-Net and Conditional Random Fields 3D CNN of CNS ensemble method the baseline modlle with an accuracy score is found to be 98.98 % precision of (0.9885), recall (0.9896)and F1 sccore(0.9893). Compared to current state-of-the-art methods, these metrics are improvement of approximately 1–5% in accuracy. The ResNet152 based method proposed in this study is an excellent effort for enhancing the diagnosis of brain tumors, and which aims to become beneficial as a more effective tool designed to achieve better clinical quality outcome.