Utilizing Deep improved ResNet50 for Brain Tumor Classification Based MRI

A robust approach for brain tumor classification is being developed using deep convolutional neural networks (CNNs). This study leverages an open-source dataset derived from the MRI Brats2015 brain tumor dataset. Preprocessing included intensity normalization, contrast enhancement, and downsizing. Data augmentation techniques were also applied, encompassing rotations and flipping. The core of our proposed approach lies in the utilization of a modified ResNet-50 architecture for feature extraction. This model integrates transfer learning by replacing the final layer with a spatial pyramid pooling layer, enabling it to leverage pre-trained parameters from ImageNet. Transfer learning from ImageNet aids in countering overfitting. Our model's performance was evaluated with various hyperparameters, including existing methods in terms of accuracy, precision, recall, F1-score, sensitivity, and specificity. This study showcases the potential of deep learning, transfer learning, and spatial pyramid pooling in MRI-based brain tumor classification, providing an effective tool for medical image analysis. Our methodology employs a modified ResNet-50 architecture with transfer learning, integrating a spatial pyramid pooling layer for feature extraction. Systematic evaluation showcases the model's superiority over existing methods, demonstrating remarkable results in accuracy (0.9902), precision (0.9837), recall (0.9915), F1-score (0.9891), sensitivity, and specificity. The comparative analysis against prominent CNN architectures reaffirms its outstanding performance. Our model not only mitigates overfitting challenges but also offers a promising tool for medical image analysis, underlining the combined efficacy of spatial pyramid pooling and transfer learning. The study's optimization parameters, including 25 epochs, a learning rate of 1e-4, and a balanced batch size, contribute to its robustness and real-world applicability, furthering advancements in efficient brain tumor classification within MRI data.