Advancing Brain Tumor Classification with Deep Learning

Abstract

One of the most serious and potentially fatal neurological conditions, brain tumors must be identified early and accurately to be effectively treated. The analysis of MRI scans by hand is the foundation of conventional diagnostic methods, which can be laborious and prone to human error. In this work, we use Convolutional Neural Networks (CNNs) to present a deep learning-based method for automated brain tumor categorization. Our classifier is trained on the Kaggle Brain Tumor Classification MRI dataset, which includes four tumor categories: glioma, meningioma, pituitary tumor, and no tumor. Comprising numerous convolutional, pooling, and dense layers, the CNN design is optimized using the Adam optimizer and the categorical cross-entropy loss function. With its 98.81% training accuracy and 90.48% validation accuracy, the model shows its capacity to distinguish among several tumor kinds. Performance is assessed with reference to accuracy measurements, loss curves, and confusion matrices. According to our research, deep learning-based tumor classification can significantly increase diagnostic efficacy by reducing the requirement for human interpretations. Further advancements can be achieved by merging more sophisticated designs, such as ResNet, EfficientNet, or Transformer-based vision models, with more extensive and varied data, which may find application in computer-aided diagnostic (CAD) systems. The foundation for future research in AI-driven medical diagnostics is laid by this study.