A Comprehensive AI-ML Study on Enhanced Classification of Benign and Malignant Cells in Brain MRI

AI-ML integration into medical diagnostics will revolutionize the way cellular formations in brain MRI are recognized and classified. This research article focuses on the creation and validation of highly-sophisticated AI-ML models specifically designed to accurately distinguish between benign and malignant cells within MRIs of the brain, with an intention of contributing to the early detection and treatment strategies for brain tumors. The work starts with an AI-ML framework that is based on deep learning and convolutional neural networks, which is used to analyze brain MRI data with high accuracy. This approach is constructed to recognize minor trends and differences suggestive of cancers and is quite accurate in its detection. Validation on a set of huge MRI scans of the brain shows that the model outperforms current diagnostic methods for many metrics such as accuracy, precision, recall, and F1-score. Data handling, model training as well as ethical challenges are explored in detail which reveals the complexity of implementation of AI-ML in the medical imaging area. The proposed model is integrated into existing clinical workflow which in turn gives it a place in assisting diagnosis and decision making by the healthcare workers. The results emphasize the effect of AI-ML technologies on the improvement of the accuracy and the speed of the targeting of brain cells in MRI scans. The presented research shows that AI-ML has a high potential to improve the diagnostic process and therefore increases the patient care outcomes in neuro-oncology.