Deep Learning-Enhanced MRI for Brain Tumor Detection and Characterization

Brain tumors are a critical healthcare concern, demanding accurate and timely diagnosis for effective treatment planning. Magnetic Resonance Imaging (MRI) is a valuable tool for non-invasive brain tumor assessment. This study presents a novel approach, Deep Learning-Enhanced MRI (DE-MRI), which combines the power of AutoEncoder and Residual Neural Network (ResNet) architectures for improved brain tumor detection and characterization. In the DE-MRI framework, an AutoEncoder is employed to extract meaningful features from MRI images, reducing data dimensionality while preserving critical information. These features are then fed into a ResNet-based neural network, enabling high-level feature learning and classification. This two-stage architecture enhances the model's ability to differentiate between normal brain tissue and tumor regions, as well as categorize tumors into various types and grades. Whenever compared to conventional MRI-based tumor identification methods, the suggested DE-MRI methodology shows promising results in terms of accuracy, sensitivity, and specificity. Additionally, it offers the advantage of reduced manual intervention and faster diagnosis, potentially leading to more timely and tailored treatment plans for patients with brain tumors. The AutoEncoder-ResNet hybrid model distinguishes itself by achieving the highest accuracy of 0.96 and the most minimal loss of 1.46. Overall, DE-MRI, with its innovative combination of AutoEncoder and ResNet, represents a promising advancement in the field of medical imaging for brain tumor diagnosis and characterization, contributing to improved patient outcomes and healthcare efficiency.