Explainable Artificial Intelligence for Deep Learning Models in Diagnosing Brain Tumor Disorder

Deep neural networks (DNNs) have shown great potential in diagnosing brain tumor disorder, but their decision-making processes can be difficult to interpret, leading to concerns about their reliability and safety. This paper presents overview of explainable artificial intelligence techniques which have been developed to improve the interpretability and transparency of DNNs and have been applied to diagnostic systems for such disorders. Based on the utilized framework of explainable artificial intelligence (XAI) in collaboration with deep learning models, authors diagnosed brain tumor with the help of convolutional neural network and interpreted its outcomes with the help of numerical gradient-weighted class activation mapping (numGrad-CAM-CNN), therefore achieved highest accuracy of 97.11%. Thus, XAI can help healthcare professionals in understanding how a DNN arrived at a diagnosis, providing insights into the reasoning and decision-making processes of the model. XAI techniques can also help to identify biases in the data used to train the model and address potential ethical concerns. However, challenges remain in implementing XAI techniques in diagnostic systems, including the need for large, diverse datasets, and the development of user-friendly interfaces. Despite these challenges, the potential benefits for improving patient outcomes and increasing trust in AI-based medical systems make it a promising area of research.