Explainable CNN for brain tumor detection and classification through XAI based key features identification

Despite significant advancements in brain tumor classification, many existing models suffer from complex structures that make them difficult to interpret. This complexity can hinder the transparency of the decision-making process, causing models to rely on irrelevant features or normal soft tissues. Besides, these models often include additional layers and parameters, which further complicate the classification process. Our work addresses these limitations by introducing a novel methodology that combines Explainable AI (XAI) techniques with a Convolutional Neural Network (CNN) architecture. The major contribution of this paper is ensuring that the model focuses on the most relevant features for tumor detection…