Detecting Glioma, Meningioma, and Pituitary Tumors, and Normal Brain Tissues based on Yolov11 and Yolov8 Deep Learning Models

Detecting Glioma, Meningioma, and Pituitary Tumors, and Normal Brain Tissues based on Y olov11 and Y olov8 Deep Learning Models Ahmed M. Taha a, Salah A. Aly b,c, Mohamed F. Darwish d a Dept. of CE, Faculty of Engineering, Egypt University of Informatics, Cairo, Egypt b Faculty of Computing and Data Science, Badya University, Giza, Egypt c CS&Math Branch, Faculty of Science, Fayoum University, Fayoum, Egypt d Dept. of Pathology, Faculty of Medicine, Badya University, Giza, Egypt Abstract --Accurate and quick diagnosis of normal brain tissue Glioma, Meningioma, and Pituitary T umors is crucial for optimal treatment planning and improved medical results. Magnetic Resonance Imaging (MRI) is widely used as a non-invasive diagnostic tool for detecting brain abnormalities, including tumors. However, manual interpretation of MRI scans is often time-consuming, prone to human error, and dependent on highly specialized expertise. This paper proposes an advanced AI-driven technique to detecting glioma, meningioma, and pituitary brain tumors using Y oloV11 and Y oloV8 deep learning models. Methods: Using a transfer learning-based fine-tuning approach, we integrate cutting-edge deep learning techniques with medical imaging to classify brain tumors into four categories: No-T umor, Glioma, Meningioma, and Pituitary T umors.