Transformative Approaches in Brain Tumor Detection: Harnessing Deep Learning from MRI Data

This research uses deep learning algorithms on Magnetic Resonance Imaging (MRI) data to address the urgent need for accurate and effective brain tumour diagnosis. The increasing prevalence of brain tumors underscores the importance of robust and automated diagnostic tools. CNN, a subset of deep learning, have demonstrated effectiveness in image analysis tasks, making them well-suited for medical image classification. The study commences with the acquisition and preprocessing of a diverse dataset encompassing both normal and tumor-affected brain MRI images. The data is meticulously curated, normalized, and augmented to enhance the model’s generalization capabilities. A novel deep learning architecture, inspired by established networks such as VGG and ResNet, is proposed for tumor detection. The model is trained on the preprocessed dataset, employing appropriate loss functions and optimization algorithms. Rigorous validation procedures ensure that the model avoids overfitting to the training data, while hyper-parameter tuning optimizes its overall performance. Thorough testing on a separate dataset evaluates the model’s generalization ability and accuracy in detecting brain tumors. Through the implementation of this approach, the CNN classifier achieves an impressive accuracy of approximately 94.1%.