YOLO-Driven Adaptive Deep Neural Architecture for Automated Brain Tumor Segmentation in MRI Imaging

Adults are at serious risk for brain tumours, which are characterised by the fast growth of aberrant brain cells that can seriously impair organ function or even result in death. The sizes, textures, and locations of these tumours vary greatly. We showed that using transfer learning to fine-tune a state-of-the-art YOLO model greatly enhanced its ability to identify pituitary brain tumours, meningiomas, and gliomas. Because small tumour identification is so difficult, research into brain tumour identification must continue, and our detection technologies must be continuously improved. Our suggested deep learning algorithm had encouraging outcomes, correctly detecting brain tumours in MRI scans and pinpointing their exact location. We hope to improve the diagnostic tools available to patients and healthcare professionals in the difficult fight against brain malignancies.