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@article{207110,
        author = {Nisha Ranjan and Prachi and Shweta Vishwakarma and Akash Sanghi},
        title = {Early Detection of Alzheimer’s Disease Using Deep Learning on MRI Images: A Comparative Study of CNN And ResNet50},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {69-75},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207110},
        abstract = {Alzheimer’s disease is a progressive neurodegenerative disorder that affects memory, cognitive abilities, and daily functioning. Early detection is crucial for timely intervention and effective disease management. Traditional diagnostic methods are often time-consuming and may fail to identify the disease in its early stages. In recent years, deep learning has emerged as a powerful approach for medical image analysis, particularly in the classification of brain MRI images. This study presents a comparative analysis of deep learning models, specifically a custom Convolutional Neural Network (CNN) and transfer learning using ResNet50 model, for the early detection of Alzheimer’s disease. The dataset consists of MRI images categorized into four classes: non-demented, very mild, mild, and moderate Alzheimer’s. Data preprocessing techniques such as normalization, resizing, and augmentation were applied to enhance model performance and reduce overfitting. The experimental results demonstrate that the CNN model achieved an accuracy of 75.22%, outperforming the ResNet50 model, which achieved 51.52%.
Additional evaluation metrics, including precision, recall, and F1-score, further confirm the superior and balanced performance of the CNN model. The results highlight that custom CNN architectures are more effective in handling limited and imbalanced medical image datasets compared to transfer learning approaches. This study provides a comparative insight into model performance under imbalanced data conditions and emphasizes the importance of model selection in medical imaging applications. The findings demonstrate the potential of deep learning techniques in supporting early diagnosis and improving healthcare decision-making systems.},
        keywords = {Alzheimer’s Disease, Deep Learning, MRI Imaging, Convolutional Neural Network (CNN), ResNet50, Early Detection, Medical Image Analysis.},
        month = {July},
        }