Automated Classification of Pediatric Posterior Fossa Tumors Using Machine Learning and Deep Learning on MRI Data

This project aims to develop advanced machine learning and deep learning models to help radiologists and clinicians accurately identify and differentiate types of posterior fossa tumors in children using MRI scans. These tumors are relatively rare but represent a significant portion of pediatric brain tumors and have various subtypes with differing treatment protocols and prognoses. With the support of the Children’s Brain Tumor Network (CBTN) MRI data, we aim to enhance our model’s reliability and accuracy, potentially leading to quicker, more accurate diagnoses. This project could improve the clinical decision-making process and patient outcomes by providing an efficient, non-invasive diagnostic tool.