A Deep Learning-Based Automated Medical Diagnosis on Cloud Platforms: A Comparative Study of Brain Tumor Classification and Identification

Accurate classification and detection of brain tumors on medical imaging is essential for diagnosis and treatment planning. As deep learning models can learn discriminatory properties from raw data, they have shown notable success in various medical imaging applications including brain tumor classification Furthermore, cloud computing resources enable scalability and accessibility, enabling sophisticated deep learning models to be used for performing medical image processing tasks. This paper presents an in-depth analysis of brain tumor classification and detection using deep learning models established in a cloud environment. We explore how deep learning algorithms such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and a combination thereof can be used to accurately distinguish specific types and grades. AI-driven techniques have the potential to greatly improve treatment planning since machine learning algorithms can evaluate massive datasets of patient outcomes and treatment protocols and suggest individualized treatment plans based on the distinct qualities and medical histories of each patient. This tailored strategy has the potential to maximize resource use, reduce side effects, and enhance therapeutic success. In the field of personalized medicine, artificial intelligence (AI) makes it easier to create prediction models that classify patients according to their genetic composition, lifestyle choices, and exposure to the environment. This allows medical professionals to provide more focused treatments and preventative measures. Healthcare professionals may proactively identify patients who are at a high risk of acquiring specific illnesses and take preventive measures to minimize these risks by utilizing AI-driven predictive analytics. By using AI-driven solutions, administrative tasks—which are frequently hampered by manual procedures and inefficiencies—can be simplified and streamlined. By automating coding, billing, and documentation processes, natural language processing algorithms lower administrative burdens and free up more time for medical staff to provide patient care.