The foremost central body part is the brain in human nervous system. It accommodates 86 billion nerve cells. Brain tumors are dangerous magnification of abnormal cells that can be homicidal in the human brain. Some common symptoms of brain tumors may comprise: Headache, Seizures, nausea, vomiting, vision changes and numbness, and some other issues. The challenge of segmenting brain tumors manually is huge, so a method for instinctively segmenting is required. MRI images are often used in the early stages to identify brain tumors using convolutional neural networks (CNN), which lean on medical imaging data. The Important challenges in computer-assisted diagnosis (CAD) for medical purposes encompass brain tumor segmentation and identification. This study aims to evaluate and compare the accuracy of pre-trained DL models for grouping brain MRI images, especially AlexNet is 98.95, Google Net is 99.4, VGG16 is 99.0, and YOLO V7 is 99.5. The analysis evaluates a variety of unique purposes, such as computational effectiveness, model complexity, and classification accuracy. A seldom used technique for brain tumor detection uses deep learning-based methods like the YOLO NAS (You Only Look Once) family of models. Enhance brain tumor segmentation accuracy by analyzing and implementing the Red Fox Optimization. The accuracy of pre-trained YOLO-NAS object identification and segmentation model is 99.7, to generate an effective Brain tumor detection in an early-stage.