Deep Learning Technique to detect Brain tumor disease using YOLO v8

Brain tumor identification is a vital step in medical imaging that identifies abnormalities in the brain. The abstract provides an overview of the techniques and procedures used in brain tumor identification using several imaging facilities such as MRI, CT scans, and PET scans. It emphasizes the importance of automated detection approaches enabled by advances in machine learning and deep learning algorithms. These techniques include preprocessing processes, feature extraction, and classification strategies for reliably distinguishing tumor areas from healthy brain tissues. Noise, diversity in tumor form, and computing complexity are highlighted, as well as continuing research attempts to improve detection accuracy and efficiency. The abstract emphasizes the need of robust and effective brain tumor detection technologies in clinical practice to assist early diagnosis, treatment planning, and monitoring of patients with brain diseases. Brain tumor identification is a critical component of neuroimaging for early diagnosis and therapy planning. Furthermore, the YOLOv8 architecture has been used to brain tumor identification, proving its ability to effectively localize and identify lesions inside medical pictures. Using YOLOv8, we made significant advances in real-time detection capabilities and model with accuracy of 96.4% allowing for more efficient diagnosis and treatment planning for patients with brain tumors. The abstract also investigates the potential of radiogenomics, which combines radiological and genomic data to uncover the underlying molecular features of brain. Finally, the abstract highlights the translational implications of brain tumor detection technologies, emphasizing their importance in improving patient outcomes and expanding our understanding of brain tumor biology.