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@article{186671,
        author = {Sukruthi K Kowshik and Sparsha S and Keshav Yadav and Shubha V C and Ravi Kumar D},
        title = {AlzAware: An AI-Based System for Early Detection of Alzheimer’s Disease Using Multimodal Data and Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {2208-2214},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186671},
        abstract = {Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder marked by gradual cognitive decline and memory loss. Early-stage identification of AD is a global medical challenge, as symptoms often overlap with natural aging or mild cognitive impairment. This paper presents AlzAware, an intelligent multimodal system that facilitates early detection of Alzheimer’s Disease using both MRI imaging and cognitive behavioral tests. The proposed framework integrates a fine-tuned ResNet50 deep learning model for MRI analysis with a cognitive assessment module powered by machine learning algorithms. The system provides two interactive dashboards — one for patients and another for doctors — enabling complete diagnostic support, from cognitive testing and MRI-based screening to doctor consultation and report generation. The model achieves high classification accuracy across four dementia stages: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented, with overall ROC-AUC values exceeding 0.99. The integration of explainable AI tools such as Grad-CAM and SHAP ensures transparency and interpretability. The AlzAware platform contributes toward affordable, accessible, and explainable Alzheimer’s detection suitable for clinical and telemedicine applications.},
        keywords = {Alzheimer’s Disease, Deep Learning, MRI Classification, Cognitive Assessment, Explainable AI, ResNet50, Multimodal Diagnosis.},
        month = {November},
        }