Brain Tumor Detection using VGG16 CNN with Data Augmentation

Brain tumors represent a critical condition characterized by the abnormal growth of cells in specific brain regions, potentially forming life-threatening masses. Early diagnosis is crucial for prompt treatment, yet the laborious and time-consuming human analysis of magnetic resonance imaging (MRI) can cause significant delays. Recent advancements in deep learning have revolutionized healthcare and medical diagnostics, offering promising solutions to these challenges. Given the constraint of limited datasets, improving deep learning algorithms, particularly convolutional neural networks (CNNs), becomes essential. Data augmentation techniques effectively address dataset limitations, enabling the training of various CNN architectures such as VGG16, EfficientNetB0, MobileNetV2, and DenseNet121. These models are compared using evaluation metrics like accuracy and other standard classification metrics, with the VGG model demonstrating superior performance, achieving a notable 97% accuracy rate. This research underscores the potential of deep learning and CNNs in accelerating brain tumor identification from MRI images, emphasizing the advantages of different CNN architectures in medical applications.