Comparison of Deep Learning Methods for Detecting Brain Tumor

The intricate nature of brain tumors and their resemblance to normal brain tissues present significant challenges in effectively distinguishing and categorizing them through traditional medical image processing methods. Brain tumors pose a considerable global health risk, often leading to inaccurate diagnoses and prognoses, even with human assistance. Recent advancements in deep learning have significantly enhanced the accuracy of brain tumor detection and classification using magnetic resonance imaging (MRI) of the brain. In this research, we provide an illustrative approach for the classification of brain tumors, employing transfer learning and convolutional neural networks (CNNs). We explore several CNN architectures, including MobileNet-V2, ResNet, and Xception. Notably, MobileNet-V2 emerged as the most effective model, achieving an impressive Fl score of 98.45%. The accuracy of brain tumor categorization is pivotal for the effective evaluation of tumors and the formulation of appropriate treatment strategies. MRI is a preferred imaging modality due to its superior image quality and safety, as it does not involve ionizing radiation, unlike other imaging techniques. The integration of computational intelligence, particularly deep learning (DL), has revolutionized the diagnosis and categorization of complex clinical conditions like brain tumors. Deep learning has consistently demonstrated outstanding performance in the partitioning and classification of brain tumors. In this paper, we introduce an AI-driven method for the classification of brain tumors into benign and malignant categories using deep learning algorithms. To conduct this study, publicly available datasets comprising brain images are utilized, subject to in-depth analysis and assessment to enhance the accuracy of brain tumor classification