Deep Learning Approaches for Brain Tumor Detection in MRI Scans: A Comparative Study of DenseNet and ResNet101 Architectures

Brain tumors pose a serious threat to healthcare, requiring early discovery in order to plan treatments and manage patients. This work presents a novel method for identifying brain cancers in MRI images by utilizing DenseNet and ResNet101, two cutting-edge deep learning architectures. By utilizing various strategies for augmenting data, the models become more resilient and broadly applicable. Principal Component Analysis (PCA) is also used to simplify feature space dimensions, which makes processing and model training more effective. Then, pre-trained models for brain tumor detection are modified by transfer learning, leading to better performance and less computational complexity. This research is particularly interesting because it evaluates DenseNet's performance in terms of important performance measures including accuracy, F1 score, and precision. The results show that DenseNet performs better than traditional techniques and even ResNet101, demonstrating its effectiveness in precisely locating brain tumors in MRI scans. These findings highlight DenseNet's potential to greatly increase clinical decision-making and diagnostic accuracy in medical image processing tasks, especially when deep learning concepts are incorporated.