Deep Learning with Explainable AI for Brain Tumor Diagnosis from Multimodal Imaging

Brain tumours are a serious health concern that require timely and accurate diagnosis for effective treatment. This study explores the use of deep learning models combined with multimodal imaging data, such as MRI, CT, and PET scans, to accurately detect and classify brain cancers. We present a novel framework for efficiently analysing multimodal imaging data and extracting complex features by integrating advanced deep learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). To enhance the interpretability and clinical trust in the model's predictions, we apply Explainable AI (XAI) techniques. By identifying the most significant areas in the images that influence diagnosis, tools such as Grad-CAM and SHAP illuminate the model's decision-making process. Our findings show a significant improvement over conventional methods, with high levels of specificity, sensitivity, and accuracy in identifying and categorizing different types of brain tumours’ helping radiologists understand the model's predictions, the inclusion of XAI not only enhances the model's transparency and reliability but also enables better clinical decision-making. This study provides a robust and comprehensible framework for precise brain tumour identification, advancing the field of medical image analysis and ultimately improving patient outcomes.