Efficient Classification of Brain Tumor Images Using Neural Network Technique

Abstract

Brain tumor identification and categorization are critical for timely medical intervention and patient care. This paper proposes a unique strategy for the effective classification of brain tumor images by leveraging the power of deep learning. We suggest using neural network techniques, notably Convolutional Neural Networks (CNNs), to automate brain tumor image categorization, improving both diagnostic accuracy and efficiency. Our study begins with the collecting and pre-processing of a large dataset of brain tumor photos to ensure consistency and quality. The dataset is painstakingly divided into training, validation, and test sets. Our process is built around a carefully selected neural network architecture that has been refined through hyperparameter tweaking. To ensure robust model performance, we add critical architectural components like as convolutional layers, pooling layers, and fully connected layers, as well as dropout and batch normalization. To prevent overfitting, the neural network is trained by minimizing a suitable loss function, such as cross-entropy, while monitoring validation measures. Our model's performance is thoroughly validated on an independent test dataset using a variety of measures such as accuracy, precision, recall, F1-score, and confusion matrices. Comprehensive visualization and post-processing tools help to refine the classification results even further. This study intends to advance medical image analysis by providing a fast and precise method of identifying brain tumor images, thereby benefiting healthcare professionals in their decision-making processes. By conducting various experiments on our proposed model we finally came to conclusion that our proposed Vgg-19 model achieved more accuracy compared with several other CNN models.