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@article{194052,
        author = {Neha Sahu and Mrs. Madhavi Kshatri and Dr. Asha Ambhaikar},
        title = {Classification of Alzheimer’s Disease Stages Using MRI Images and Convolutional Neural Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {2683-2690},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194052},
        abstract = {Alzheimer’s disease is a progressive neurodegenerative disorder that affects memory, thinking, and behavior, ultimately interfering with daily life. Early and accurate diagnosis plays a crucial role in managing symptoms and improving patient care. In this study, we explore the use of Convolutional Neural Networks (CNNs) to classify brain MRI images into four distinct stages of Alzheimer’s disease: Mild Demented, Moderate Demented, Non-Demented, and Very Mild Demented. The dataset used for this purpose consists of pre-labeled MRI scans collected from publicly available sources, ensuring diverse representation and authenticity. After preprocessing the images and applying appropriate data augmentation techniques, the CNN model is trained to automatically recognize patterns associated with each stage of the disease. The results demonstrate promising classification accuracy across different categories, showcasing the potential of deep learning approaches in assisting radiologists and clinicians with early diagnosis and decision-making. This work highlights how artificial intelligence, when integrated with medical imaging, can enhance the accuracy and efficiency of diagnostic processes.},
        keywords = {Alzheimer’s disease, MRI image classification, Convolutional Neural Network (CNN), deep learning, neuroimaging, medical diagnosis, artificial intelligence.},
        month = {March},
        }