Abstract:

Brain tumors pose a significant health challenge worldwide, necessitating accurate and timely diagnosis for appropriate treatment planning. Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique widely used for brain tumor detection and classification. This paper presents an innovative approach to detect and classify brain tumors as normal and abnormal tumors in MRI images, utilizing advanced deep learning techniques. The proposed methodology consists of three main stages: preprocessing, feature extraction, and classification. During preprocessing, the MRI images are enhanced and normalized to ensure consistent data quality. Subsequently, an advanced feature extraction method based on convolutional neural networks (CNNs) is employed to automatically extract relevant features from the MRI images. These features encapsulate the intricate patterns and characteristics associated with both normal brain tissue and different types of brain tumors.

Description:

Brain, Tumors, MRI, Edge, Scan