Classification of Brain Tumors Using Augmented MRI Images and Deep Learning

For early diagnosis and treatment planning, medical image analysis is crucial, especially the automated detection and classification of brain cancers. We offer an approach based on deep learning for the classification of brain tumors caused by magnetic resonance imaging (MRI) data in this work. Our approach uses a pre-trained VGG16 convolutional neural network (CNN) architecture that was honed using a dataset of MRI images that included cases of pituitary tumors, meningiomas, gliomas, and non-tumor cases. We use data augmentation approaches, such as brightness and contrast modifications, resulting in enhanced training data diversity, to strengthen the model's robustness and improve generalization. Our CNN is trained using the updated dataset, and it performs admirably in accurately classifying brain cancers. Our experimental results, which achieved high classification accuracy on a different test dataset, show the effectiveness of our method. We give a thorough evaluation of the performance of model, including metrics for accuracy, precision, F1-score, and recall with accuracy of 91%. We also display the training history, emphasizing the model's convergence and generalization. This study advances the field of medical image analysis by offering an automated brain tumor classification method based on deep learning. The suggested method can help doctors identify and diagnose brain cancers early, which will ultimately improve patient outcomes. The data augmentation methods used in this work can also be helpful for tasks like medical picture categorization, providing a way to enhance model performance and dependability.