A 3D Deep Learning Framework for Brain Tumor Classification Followed by Region-Aware Segmentation Using UNetR

Properly segmenting a brain tumor from MRI images is needed for correct diagnosis, planning the best treatment and following patients' progress. The paper describes a mix of 3D CNNs and the Transformer-based UNETR architecture to help accurately and automatically segment brain tumors. It starts with first correcting biases, normalizing and resizing the volumetric BRATS dataset to make it standard. Using the 3D CNN, the preprocessed data is examined to identify important spatial patterns in anatomical structures. With the UNETR model, these features and labels are improved further by using a transformer encoder to spot important context and a U -Net-style decoder to ensure exact segmentation. The results of the proposed method were very impressive, reaching an accuracy of 99.21 %, a recall of 99.42%, a precision of 99.15%, and an F1-score of 99.17%. Using this technique could greatly improve clinical procedures by automatically and reliably segmenting brain tumors.