A Novel Method for Prompt Brain Tumor Identification Utilizing Augmented Data and Deep CNN

Brain tumor detection is a significant challenge in the medical domain due to the complexity and variability of MRI images. The trained model addresses the need for accurate and early diagnosis by proposing an advanced deep learning model that integrates data augmentation with Convolutional Neural Networks (CNN) to enhance tumor classification. Unlike traditional approaches, this work emphasizes targeted augmentation techniques such as rotation, flipping, zooming, and contrast normalization to increase data diversity and improve model generalization. The novelty of the proposed system lies in its robust preprocessing pipeline and customized CNN architecture that not only improves feature extraction but also minimizes overfitting on limited medical datasets. The model is trained on the Kaggle Brain Tumor MRI dataset, which includes 3,264 images categorized into glioma, meningioma, pituitary tumor, and no tumor. Images are resized to 224 × 224 pixels, normalized, and labeled using one-hot encoding. The CNN architecture consists of multiple Conv2D, MaxPooling, and dense layers, optimized with the Adamax optimizer for stable convergence. Classification is treated as a multi-class problem using a softmax activation in the output layer. The system achieves a classification accuracy of 98%, demonstrating its capability to support radiologists in reliable brain tumor diagnosis. This work stands out due to its integration of augmentation and architecture optimization tailored specifically for medical imaging.