An Enhance CNN Model for Brain Tumor Detection from MRI Images

Abstract

A Convolutional Neural Network (CNN) model for the precise classification and detection of brain tumors from Magnetic Resonance (MR) images is presented in this research. Leveraging the power of deep learning, the proposed CNN model demonstrates remarkable accuracy in distinguishing between different tumor types, aiding in effective diagnosis. The network is trained on a diverse dataset, enabling robust generalization to unseen cases. The proposed method not only enhances the classification performance but also facilitates early tumor detection. Operating on 256 x 256 image inputs, the proposed model boasts a complex architecture featuring six Conv2D layers, batch normalization, five MaxPooling2D layers, six dense layers, and an input layer. Throughout these layers, the ReLU activation function dominates with the final dense layer utilizing the sigmoid function. The potential of sophisticated CNN architectures in medical imaging is highlighted by this study. The outcomes validate the effectiveness of the CNN-based method, advancing computer-aided neuroimaging diagnostic tools and offering a useful tool for medical practitioners. The increased accuracy values demonstrate how well the sophisticated CNN model recognizes brain tumors and point to its potential as a more complex and dependable technique. It also presents a viable avenue for accurate and timely brain tumor identification.