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@article{177962,
        author = {ULAVA GANGOTHRI NAIDU and NEELAKANTAM SURYA TEJA and GATALA SATHISH and DUVVURU DEEPTHI and GUNJI BUELA and M BRAHMA KOWSHIK KUMAR},
        title = {A Survey on Detection of Alzheimer’s Disease from Brain MRI Scans},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1733-1741},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177962},
        abstract = {Health Coder is a research project aimed at classifying Alzheimer's disease using AI and brain MRI images to help in early diagnosis. The project uses a dataset of 6400 preprocessed MRI images, each resized to 128 x 128 pixels, representing different stages of Alzheimer's. The goal is to develop an AI model that can identify the disease's progression. To achieve this, the MRI images will be preprocessed to improve their quality, reduce noise, and standardize the data. Deep learning models, trained with TensorFlow and Keras, will then classify the images into various stages of Alzheimer's. The models will be optimized for better performance, considering metrics like accuracy, precision, and recall. The performance of different models will be evaluated and compared to determine the most effective one. The results, including the MRI images, predictions, and metrics, will be visualized for easier analysis. The project will also include a detailed report summarizing the methodology, results, limitations, and possible future improvements.
Alzheimer’s Disease (AD) is characterized by the gradual degeneration and decline of brain cells, leading to irreversible neurological changes. This study investigates advanced image enhancement techniques for improving AD diagnosis using brain MRI. The methods used include CLAHE (Contrast Limited Adaptive Histogram Equalization) to enhance local image contrast and ESRGAN (Enhanced Super Resolution Generative Adversarial Networks) to improve image resolution. These preprocessing methods improve MRI images and classification accuracy. An ensemble model of MobileNetV2 and DenseNet121, two efficient deep-learning models with feature extraction capabilities, were used as classifiers.},
        keywords = {Alzheimer’s, computer-aided diagnosis, deep learning, DenseNet121, image enhancement, MobileNetV2, Transfer learning, Conventional neural networks, CLAHE, ESRGAN.},
        month = {May},
        }