Enhancing Brain Tumor Classification Accuracy Using Deep Learning with Real and Synthetic MRI Images

Brain tumors, being the most severe and complex kind of cancer, necessitate specialized investigation for diagnosis, treatment, and care. Early recognition of brain tumors enhances patient care and reduces mortality rates. The application of deep learning in MRI diagnostics has transformed medicine. The study employs real and synthetic MRI data to evaluate novel deep-learning models to enhance brain tumor diagnosis. The ensemble model employed AlexNet, VGG16, and ResNet 18 on MR data from Rizgary Hospital in Erbil and Hiwa Hospital in Sulemani, as well as synthetic images produced by Deep Convolutional Generative Adversarial Networks. Modeling measurements encompassed accuracy, precision, recall, and F1 score. The architecture of ResNet18 and its capacity to incorporate residual connections for feature mapping enabled it to surpass all other models in classification accuracy, achieving 99%. Although AlexNet and VGG16 achieve accuracies of 98.16% and 98.83%, respectively, ResNet18 excels in differentiating between normal and unusual instances. DCGANs excel in generating synthetic images and enhancing image categorization and model precision. A study utilizing both real and synthetic images showed that synergistic virtual paradigms could enhance the accuracy of clinical instruments and facilitate deeper AI integration in medicine. Subsequent research will focus on optimizing model architecture and implementing data augmentation techniques to enhance classification accuracy. This Python study demonstrates that deep learning can enhance the diagnosis and treatment of brain tumors.