Paper Title
Brain Tumor Detection Using Image Processing and AI & ML

Abstract
Brain tumors pose a serious threat to human health due to their complexity and potential to cause irreversible damage if not detected early. Accurate and timely diagnosis plays a critical role in effective treatment planning and patient outcomes. This research focuses on the development of an automated system for brain tumor detection using advanced image processing techniques integrated with artificial intelligence and machine learning (AI/ML) models. Pre-processing steps such as skull stripping, noise removal, and contrast enhancement are employed to improve the quality of MRI images. Feature extraction methods are then applied to highlight tumor-specific characteristics, followed by the use of supervised machine learning algorithms, including Convolutional Neural Networks (CNNs), for classification and segmentation of tumor regions. The proposed approach demonstrates high accuracy and reliability in identifying various types of brain tumors, thereby assisting radiologists in making informed decisions. Experimental results show that combining image processing with AI/ML significantly improves detection efficiency and reduces human error. This work contributes to the advancement of computer- aided diagnosis systems in medical imaging. Keywords - Image Processing, MRI, Artificial Intelligence (AI), Machine Learning (ML), Convolutional Neural Networks (CNN), Tumor Detection, Medical Imaging, Feature Extraction, Classification, Segmentation, Computer- Aided Diagnosis, Tumor Segmentation, Radiology Automation