Brain Tumor Detection Using Convolutional Neural Network

Abstract

Abstract:

Brain tumor detection is a critical task in medical imaging, as early diagnosis can significantly improve patient outcomes. Convolutional Neural Networks (CNNs) have shown remarkable success in various image recognition tasks, including medical image analysis. This paper proposes a novel approach for brain tumor detection utilizing CNNs. The proposed model leverages the inherent features of CNNs to automatically learn discriminative features from magnetic resonance imaging (MRI) scans. Preprocessing techniques such as data augmentation and normalization are employed to enhance the robustness and generalization of the model. The trained CNN is capable of accurately identifying the presence and location of tumors in brain MRI scans. Experimental results on benchmark datasets demonstrate the effectiveness and efficiency of the proposed approach compared to existing methods, showcasing its potential for aiding radiologists in diagnosing brain tumors more accurately and efficiently.