An Approach of ML & DL to Analyze Brain Tumor Prediction

Accurate brain tumor prediction in MRI images is a critical aspect of modern neuro-oncology, significantly impacting patient outcomes through early diagnosis and targeted treatment. In this paper, we explore how to improve detection and classification by applying traditional ML and DL techniques such as CNN. Integrating data augmentation, transfer learning, and advanced image pre-processing techniques has further refined model performance, enabling the practical analysis of low-quality MRI scans. However, challenges remain in ensuring model interpretability and addressing class imbalance in training datasets. This study highlights the current landscape of tumor detection methodologies using ML and DL, emphasizing models that provide high accuracy and offer transparent decision-making processes. By synthesizing findings from various research efforts, to assist the challenging task of brain tumor identification in medical science, this paper aims to contribute to the ongoing development of AI-driven diagnostic tools.