Channel-Attention DenseNet with Dilated Convolutions for MRI Brain Tumor Classification

Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations. Due to the increase in precision image-based diagnostic tools, driven by advancements in artificial intelligence (AI) and deep learning, there has been potential to improve diagnostic accuracy, especially with Magnetic Resonance Imaging (MRI). However, traditional state-of-the-art models lack the sensitivity essential for reliable tumor identification and segmentation. Thus, our research aims to enhance brain tumor diagnosis in MRI by proposing an advanced model. The proposed model incorporates dilated convolutions to optimize the brain tumor segmentation and classification. The proposed model is first trained and later evaluated using the BraTS 2020 dataset. In our proposed model preprocessing consists of normalization, noise reduction, and data augmentation to improve model robustness. The attention mechanism and dilated convolutions were introduced to increase the model’s focus on critical regions and capture finer spatial details without compromising image resolution. We have performed experimentation to measure efficiency. For this, we have used various metrics including accuracy, sensitivity, and curve (AUC-ROC). The proposed model achieved a high accuracy of 94%, a sensitivity of 93%, a specificity of 92%, and an AUC-ROC of 0.98, outperforming traditional diagnostic models in brain tumor detection. The proposed model accurately identifies tumor regions, while dilated convolutions enhanced the segmentation accuracy, especially for complex tumor structures. The proposed model demonstrates significant potential for clinical application, providing reliable and precise brain tumor detection in MRI.