Advanced AI framework for accurate detection and classification of brain tumours from MRI images

Brain tumours adversely affect patient outcomes owing to their intricacy and the difficulties associated with diagnosis. The accuracy and timeliness of diagnosis are hindered by the subjectivity and unpredictability inherent in manual magnetic resonance imaging (MRI) interpretation. We present novel research on artificial intelligence systems capable of detecting, segmenting, and categorising brain cancers utilising MRI data, which may assist in addressing these issues. The system utilises advanced convolutional neural network (CNN) designs and unique explainability methods; it is designed for application in therapeutic and evidential contexts. This approach addresses deficiencies in cancer categorisation, differentiation, and AI interpretability, hence enhancing the accuracy and reliability of diagnosis. The method's efficacy and practical utility were evidenced through validation on extensive MRI datasets encompassing gliomas, meningiomas, pituitary tumours, and healthy controls. An AI-driven diagnostic tool can increase clinical decision-making, decrease diagnostic error rates, expedite therapy initiation, and improve patient outcomes. • Artificial Intelligence and Radiologist Confidence Levels Compare the subjective confidence levels of radiologists diagnosing. • Displays the results of comparing the suggested model's performance to two well-known pre-trained architectures. • Brain tumours can be real-time detected using the proposed AI method. There are a lot of MRI slices in the picture. • MRI Scans Emphasises the areas of the MRI image that were most impacted by the classification to show how interpretable the AI model.