Brain tumor classification and detection utilizing machine learning and deep learning are increasingly becoming the focus of medical image analysis research. Thanks to the advancement of modern imaging technology and machine learning algorithms, it is now possible to accurately and efficiently classify and diagnose brain tumors using a range of methodologies. Machine learning and deep learning algorithms are currently utilized to assess medical images and categorize tumors based on traits such as shape, size, and texture.

The integration of machine and deep learning technologies in brain tumor identification brings to the forefront critical ethical and regulatory considerations. As these technologies offer promising advancements in accuracy and efficiency, concerns emerge regarding patient privacy, informed consent, and the potential impact of algorithmic biases. Striking a balance between the benefits of these innovations and safeguarding patient rights requires robust regulatory frameworks that govern data usage, sharing, and patient confidentiality. Collaboration among medical experts, technologists, ethicists, and policymakers becomes essential to ensure the responsible and transparent deployment of these technologies, aligning medical progress with ethical standards and regulatory compliance.