<![CDATA[Brain tumors are a terrible and dangerous health problem, often posing a significant threat to individuals due to their high probability of death. Detecting these tumors at an early stage is crucial, as it not only increases the chances of successful treatment but also plays a pivotal role in reducing total healthcare costs. Early detection allows medical professionals to take action quickly, enabling a more targeted and effective treatment approach. Numerous studies are currently employing Machine Learning (ML) and Deep Learning (DL) to classify brain tumors, promising improved accuracy and efficiency in tumor identification for potential breakthroughs in medical diagnosis. However, a significant challenge lies in these models being "black box" as their complex inner workings are not easily understood by humans. Explainable Artificial Intelligence (XAI) refers to the capability of an artificial intelligence (AI) system to provide understandable and interpretable explanations for its decisions or predictions. In this study, we propose a classification model based on various network architectures, namely DenseNet201, DenseNet169, Xception, MobileNetV2 and ResNet50. We then used Grad-CAM and Grad-CAM++ to interpret the model's results, evaluating its ability to distinguish important features in Magnetic resonance imaging (MRI) images of brain tumors during the decision-making process. The integration of Grad-CAM and Grad-CAM++ enhances the interpretability of the brain tumor classification model, providing valuable evidence of its effectiveness by focusing on crucial features in MRI images of brain tumors during decision-making. Research results contribute to the development of systems that support early diagnosis of tumors. This contribution is pivotal as it not only enhances the model's transparency but also validates its effectiveness in accurately identifying brain tumors.]]>
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