Brain Tumor Classification using VGG19 Deep Learning Model

Accurate and timely diagnosis of brain tumors is very crucial for appropriate treatment planning and proper management of the patient. In the past years, deep learning has influenced medical image analysis, mainly in relation to classifying tumors using medical imaging techniques. The article proposed a framework for classifying brain tumors with the use of a deep learning model based on the VGG19 architecture with MRI images sourced from the comprehensive Kaggle dataset containing 7,023 annotated images. The proposed model utilizes the strengths of deep convolutional neural networks in automatically extracting relevant features from those complex MRI images to optimize the classification process. After an extensive process of preprocessing, augmentation, and a thorough training protocol, the VGG19 model was established, such that it achieved an accuracy of $\mathbf{9 9. 1 7 \%}$ on the validation set. Such high accuracy underscores the efficacy of using deep learning approaches for medical imaging and, more importantly, serves as an injunction for further investigations into automated diagnostic systems that could be handy in supporting the clinician toward better patient outcomes and an enhancement in diagnostic workflows.