ABSTRACT

The accurate and timely diagnosis of brain tumors is crucial for effective treatment planning and patient outcomes. Magnetic Resonance Imaging (MRI) has become a cornerstone in the evaluation of brain tumors, providing detailed anatomical information with excellent soft tissue contrast. However, manual interpretation of MRI images by radiologists is time-consuming and subjective, leading to variability in diagnostic accuracy. This study aims to address these challenges by developing a Convolutional Neural Network (CNN) model for automated brain tumor diagnosis using MRI image datasets. The study involves collecting and preprocessing MRI image datasets, designing and implementing a CNN architecture for brain tumor detection and classification, training and evaluating the CNN model, and assessing its clinical implications. The significance of this study lies in its potential to advance diagnostic technology, enhance patient care, streamline diagnostic workflows, enable personalized medicine, contribute to scientific knowledge, and facilitate clinical implementation and translation. The findings of this study have implications for medical imaging, clinical practice, and patient outcomes, paving the way for improved brain tumor diagnosis and management.