Revolutionizing Brain Tumor Diagnosis With Adaptive CNN Models:

R. Kishore Kanna Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India https://orcid.org/0000-0002-8004-1501 Priyanka Singh Amity University, Noida, India https://orcid.org/0000-0003-0841-1544 S. Raju KPR Institute of Engineering and Technology, India Ayodeji Olalekan Salau Afe Babalola University, Nigeria & Saveetha Institute of Medical and Technical Sciences, Nigeria https://orcid.org/0000-0002-6264-9783 Archibald Danquah Danquah-Amoah Accra Technical University, Ghana R. Gomalavalli Sri Sai Ram institute of Technology, India https://orcid.org/0000-0003-2168-167X Sathea Sree Vels Institute of Science, Technology, and Advanced Studies, India Revolutionizing Brain Tumor Diagnosis With Adaptive CNN Models

Brain tumors affect millions of people worldwide and can be life-threatening if not detected early. These cancerous growths disrupt normal brain function and can spread to surrounding tissues. However, diagnosing brain tumors is challenging because they vary greatly in shape, size, and appearance, making manual detection difficult and time-consuming for doctors. Our research team developed an intelligent computer system using artificial intelligence to automatically identify and classify brain tumors from MRI scans. We tested three different AI models - ResNet-152, MobileNet, and DenseNet-121 - using a technique called transfer learning, which allows computers to build on existing medical knowledge.The results were impressive. The ResNet-152 model performed best, correctly identifying brain tumors 98.7% of the time with a 99.8% reliability score. DenseNet-121 achieved 96.5% accuracy with 98.6% reliability, while MobileNet reached 87.2% accuracy with 98.7% reliability. Even the smallest model performed well enough for practical use in hospitals with limited computing resources.
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