Exploring Deep Learning-Based MRI Radiomics for Brain Tumor Prognosis and Diagnosis

Advancements in deep learning (DL) techniques have brought a ray of hope in the battle against brain tumors by significantly improving their detection and segmentation in MRI scans. This research paper delves into recent studies that have introduced innovative DL models for brain tumor diagnosis and segmentation, achieving exceptional levels of accuracy, precision, recall, and AUC values. The VGG16, VGG19 and Hybrid-DANet architectures, among others, have emerged as beacons of promise, enabling automated tumor delineation and MR image segmentation. The integration of DL algorithms in clinical practice not only enhances surgical and radiotherapy planning but also plays a vital role in survival prediction and tumor subtyping. The discoveries of this study reveals the extraordinary capabilities of DL techniques in elevating brain tumor detection and segmentation accuracy, sensitivity, specificity, and F1-score. It highlights the power of embracing hyperparameter tuning to unlock optimal performance and redefine the landscape of brain tumor diagnosis and treatment. This profound progress instills a sense of warmth and optimism, as DL paves the way for more personalized and effective care for individuals battling brain tumors. With every step forward, DL empowers healthcare professionals and instills hope in patients and their loved ones, offering a brighter future where early detection and precise tumor segmentation become cornerstones of compassionate care.