Brain Tumor Detection Using Deep Learning-Based CNN Model

Brain tumors are a major menace to human life, requiring an early and accurate diagnosis for the betterment of patient health outcomes. This paper discusses an automated brain tumor detection and classification method using machine learning and MRI scan images. Pre-processing the images to remove noise and improve their quality, followed by segmentation to delineate the region of interest from the background of the images are the proposed techniques. Feature extraction techniques are used to capture vital characteristics and machine learning algorithms such as convolutional neural networks (CNNs) are used to effectively classify tumor types. The model is highly accurate when differentiating between malignant and benign tumors, thus minimizing the chances of misdiagnosis. CNN. The outcomes from the deployed system are enhanced efficiency and stability in the detection of tumors, rendering it a valuable asset for the assistance of radiologists and clinicians during clinical decision-making. The technique presented presents an efficient, computerized, and cost-saving methodology for early detection of tumors and, consequently, improved patient care and treatment planning.