Multimodal Image Fusion and Real-Time Alzheimer’s Diagnosis using 3D CNN

Multimodal Image Fusion and Real-Time Alzheimer’s Diagnosis using 3D CNN

Mr. K Ramesh Babu 1, K Kamali 2, M Pravallika 3, N Manasa 4 , O Keerthi 5

1 Asst. Prof. K Ramesh Babu, Department of Electronics and Communication Engineering,

 Vasireddy Venkatadri Institute of  Technology, Engineering College,  Namburu

 2,3.4,5,  UG Students, Department of Electronics and Communication Engineering,

Vasireddy Venkatadri Institute of Technology, Engineering College, Namburu

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Abstract - Alzheimer’s disease (AD) is a progressive neurological disorder and the leading cause of dementia, requiring early detection for effective management. This study presents a deep learning-based dual-modality classification model using MRI and PET imaging to differentiate AD from cognitively normal (CN) individuals. The OASIS-3 dataset was used to acquire MRI and PET scans, which were pre-processed and organized into patient-specific folders. The proposed model consists of two convolutional neural networks (CNNs) for feature extraction from MRI and PET images, followed by a fusion layer and fully connected layers for classification. Data augmentation and normalization techniques were applied to improve generalization. The trained model demonstrated the potential of deep learning in neuroimaging-based AD detection, aiding in early diagnosis and clinical decision-making. Future work will focus on optimizing the network architecture and expanding datasets to enhance robustness and reliability.

Keywords:  Alzheimer’s disease, MRI, PET, deep learning, convolutional neural network, neuroimaging