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@article{172698,
        author = {Mohammed Ameen and Dr. Puttegowda D and Mohammed Afwan and Mohammed Ameen and Manikanta Singh},
        title = {CerebroScan : Brain Tumor and Alzheimer Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {606-610},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172698},
        abstract = {Our project CerebroScan: Brain Tumor and Alzheimer Detection aims to transform the diagnostic process for neurological conditions by utilizing advanced image recognition technology. By leveraging a deep learning model, specifically a convolutional neural network (CNN), trained on an extensive dataset of MRI and CT scans, our system can accurately diagnose brain tumors and various stages of Alzheimer’s disease in real-time. Through a user-friendly interface, the system not only identifies the specific type of brain tumor or the stage of Alzheimer’s but also provides insights that support medical decision-making and treatment planning. With the capability to seamlessly integrate with medical imaging devices, our solution offers healthcare providers an efficient and accessible tool for monitoring patients' neurological health, enhancing diagnostic accuracy, and improving patient outcomes.
A new AI-based brain tumor and Alzheimer’s detection system, CerebroScan, is presented in this paper. The system leverages deep learning, specifically convolutional neural networks (CNNs), trained on MRI and CT scan datasets to automate the diagnostic process. With real-time image analysis, it accurately classifies brain tumors and determines Alzheimer’s stages.},
        keywords = {Convolutional Neural Networks (CNN), CT scans, Deep Learning, MRI},
        month = {February},
        }