Comparative Analysis of Brain Tumor Detection in MRI Images: A Study of Neural Networks for Enhanced Classification Performance

Medical imaging plays an important role in the early detection and treatment of various diseases, including tumor detection via Magnetic Resonance Imaging (MRI). This research paper presents an extensive investigation into the application of different neural network models for the classification of MRI images to enable tumor detection. We evaluate the performance of Backpropagation Neural Networks (BPN), Multiple layer perceptron (MLP), and Convolutional Neural Networks (CNN) in this context.Results give away that MLP achieves the highest accuracy at 96.69%, with good precision of 98.82% and specificity of 98.41%. CNN follows closely with 94.70% accuracy and notable sensitivity of 96.94%. These findings reveal the potential of neural networks, specifically CNN and MLP, to enhance the diagnostic capabilities of medical imaging technologies for faster and more accurate tumor detection, therefore leading to better patient outcomes.