A Brain Tumor Detection And Identification System Using Large Language Model

Abstract

Brain tumor represent a mass like structure of living and dead cells that grows uncontrollably inside the brain. Among the various medical issues, brain tumors are a big concern. They are the 10th leading cause of death in the developing world. About 700,000 people have brain tumors with 80% being non-cancerous and 20% cancerous. This study focuses on the development and evaluation of an OpenAI-based Large Language Model, for the automated classification and descriptive reporting of brain MRI images. The primary aim is to enhance diagnostic workflows by reducing human error, accelerating detection, classification and generating structured textual reports to assist radiographers.

This research employs OpenAI-based Large Language Model capabilities to process visual inputs and return structured textual outputs. This approach leverages its large-scale pre-training on aligned image-text pairs to perform semantic analysis, classification, and localization. The model was guided through structured prompt engineering to identify tumor type, size, and anatomical location. Experimental results demonstrated that the system achieved an overall classification accuracy of 90%, with recall scores of 85% for meningioma, 95% for glioma, and 90% for pituitary tumors, F1-scores across all classes ranged from approximately 0.88 to 0.94. These findings highlight the potential of OpenAI-based Large Language Model as a supportive diagnostic tool in medical
imaging.