The early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination of such conditions. Despite the advancements in Computer-Aided Diagnosis (CADx) systems powered by deep learning, the challenge of accurately classifying brain tumors from MRI scans persists due to the high variability of tumor appearances and the subtlety of early-stage manifestations. This work introduces a novel adaptation of the EfficientNetv2 architecture, enhanced with Global Attention Mechanism (GAM) and Efficient Channel Attention (ECA), aimed at overcoming these hurdles. This enhancement not only amplifies the model’s ability to focus on salient features within complex MRI images but also significantly improves the classification accuracy of brain tumors. Our approach distinguishes itself by meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance in detecting a broad spectrum of brain tumors. Demonstrated through extensive experiments on a large public dataset, our model achieves an exceptional high-test accuracy of 99.76%, setting a new benchmark in MRI-based brain tumor classification. Moreover, the incorporation of Grad-CAM visualization techniques sheds light on the model’s decision-making process, offering transparent and interpretable insights that are invaluable for clinical assessment. By addressing the limitations inherent in previous models, this study not only advances the field of medical imaging analysis but also highlights the pivotal role of attention mechanisms in enhancing the interpretability and accuracy of deep learning models for brain tumor diagnosis. This research sets the stage for advanced CADx systems, enhancing patient care and treatment outcomes.

The rapid proliferation of artificial intelligence (AI) in healthcare services has underscored the importance of ethical considerations. This development highlights the need to examine ethical implications, debates, concerns, and thoughts from diverse and broad perspectives. In this context, the study focuses on the ethical dimensions of AI in the healthcare domain. AI is increasingly being used in various healthcare applications, but this usage brings along ethical challenges. The aim of the research is to identify themes, trends, and critical points related to AI ethics in healthcare. Through literature review and bibliometric analyses, it is observed that AI ethics research in healthcare revolves around fundamental concepts such as ethics, AI, machine learning, healthcare services, and privacy. Additionally, the leading countries, authors, and institutions in the field are examined. The intensity of collaboration and knowledge sharing in the literature is steadily increasing. In conclusion, considering the potential benefits and challenges of AI use in healthcare, addressing ethical issues, ensuring data security, and enhancing transparency in AI decision processes are crucial. The study aims to provide a deeper understanding of AI ethics topics in the existing literature and guide future research.