Detection and Classification of Brain Tumour Using Machine Learning Techniques

Using the YOLOv8 detector model, this paper presents a machine learning approach for categorising and identifying brain tumours from MRI data. By providing precise, real-time analysis, the device is intended to greatly reduce radiologists' burden. Key measures, accuracy, recalling, the degree of sensitivity and the specificity, are used to evaluate the model once it has been trained on a publicly accessible Kaggle dataset. Additionally included is an intuitive web interface that makes it simple for users to check prediction results, interact with the model, and upload photographs. Additionally integrated is a web-based interface that makes it simple for users to upload photographs, communicate with the system, and view prediction results in an intuitive manner. The suggested method expedites the classification process for pituitary tumours, meningiomas, and gliomas while also increasing diagnostic accuracy. This technology could helpdoctors make quicker and more accurate clinical choices by utilising machine learning and real-time image processing.