Brain Tumor Prediction at an early stage using Deep Learning

Abstract

Brain tumors, characterized by the abnormal growth of cells within the intricate structure of the brain, pose a significant threat to human health. Their diverse nature, ranging from benign to malignant and exhibiting varied growth rates and locations, complicates early diagnosis and effective treatment. Delayed detection often leads to advanced stages where therapeutic interventions become less impactful, underscoring the critical need for early and accurate identification. The advent of deep learning, a subfield of artificial intelligence, offers a promising avenue for revolutionizing brain tumor prediction, particularly through the nuanced analysis of patient symptoms. Traditional diagnostic methods heavily rely on neurological examinations and advanced imaging techniques like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans. While these methods are crucial for confirming the presence and characteristics of a tumor, they often come into play after symptoms have manifested and potentially progressed. Early symptoms of brain tumors can be subtle and easily mistaken for other less serious conditions, leading to diagnostic delays. These symptoms can include persistent headaches, unexplained nausea or vomiting, vision problems (such as blurred or double vision), balance difficulties, changes in personality or behavior, seizures, and localized weakness or numbness.