Assessing the Efficiency of Advanced Deep Learning Model for Brain Tumor Diagnosis

This paper presents analysis of current developments in Convolutional Neural Network (CNN)-based brain tumor detection. It discusses various CNN architectures and their performance in detecting brain tumors from MR images. The paper highlights the use of transfer learning and the effectiveness of different pre-initialized models like Alexnet, VGG-16, and ResNet-50. It also emphasizes the importance of hyperparameter tuning for improving model performance. The evaluation comes to an end with a critical evaluation of present methodologies and suggests possible areas for upcoming studies. This paper Providing a greater understanding the function of CNN in the identification of brain tumors, surfacing the way for more efficient and precise diagnostic tools. In our experiments with Convolutional Neural Networks (CNNs), we evaluated various models, activation functions, and optimizers. Initially, we used the AdaMax optimizer, an extension of Adam for gradient descent optimization. By employing the softmax activation in the RMSProp optimizer for the final layer, we achieved a 99.74% accuracy.