Brain Tumor Detection Through Advanced Computational Methods

Abstract

Brain tumors are abnormal growth of bulk mass in the brain which might harmful or harmless, posing a challenge for evaluation due to the protective skull covering, the cranium. The prior detection and diagnosis is very important and decide the survival of the patient, if not diagnosed and treated earlier the life time of the patient is exponentially decreased which makes Detecting and predicting brain tumors efficiently is crucial for timely intervention. Neuroradiology employs various methods such as biopsy, radioactive iodine testing, and MRI reports, with MRI being the most prevalent. However, interpreting MRI reports demands expertise and time, necessitating a more efficient approach. Hence, we propose leveraging machine learning and deep learning algorithms to develop a model for brain tumor detection such as Convolutional Neural Network (CNN) for image processing and ML algorithms which take the parameters of an MRI report and predict the type of tumor for prediction part. The system is time efficient and comes in handy for the medical practitioner to analyze the brain tumor in its early stages and treat it appropriately before the situation gets out of hand and increases the lifetime of the patient.