Brain Tumor Detection

Brain Tumor Detection

Kavyashree L, Kishan G, Kushal S, Lohith Gowda T, Raju

Abstract - Brain tumor detection is a critical task in medical diagnostics, as early identification and accurate classification significantly improve treatment outcomes. This process leverages advanced imaging techniques, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), combined with computational methods to enhance diagnostic accuracy. Recently, artificial intelligence (AI) and machine learning (ML) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), have revolutionized tumor detection by enabling automated, precise, and efficient analysis of medical images. These models can identify subtle patterns and abnormalities in complex data, aiding in distinguishing between tumor types and grades. Challenges remain, including the need for large, annotated datasets, robust preprocessing techniques, and minimizing false positives or negatives. Integrating AI into brain tumor detection systems has the potential to assist radiologists, reduce diagnostic errors, and improve patient outcomes through earlier and more reliable diagnoses.