ABSTRACT
- Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive function, memory, and daily living. Early detection of AD is critical for timely intervention and improving the quality of life for patients. This project proposes an innovative approach to Alzheimer’s disease prediction using the MobileNet algorithm, a lightweight and efficient deep learning architecture. The MobileNet model is trained on medical imaging datasets, such as MRI or CT scans, to classify and predict Alzheimer’s disease at its early stages.
- The proposed system leverages the computational efficiency of MobileNet to ensure scalability and accessibility, making it suitable for deployment on resource-constrained devices like smartphones or embedded systems. The model undergoes rigorous preprocessing and training to achieve high accuracy, robustness, and generalizability. Performance evaluation is conducted using metrics such as accuracy, precision, recall, and F1-score, demonstrating the effectiveness of the proposed solution.
EXISTING SYSTEM
- The existing system for Alzheimer’s disease prediction utilizes the ResNet (Residual Network) architecture, a deep learning model renowned for its ability to handle very deep networks without suffering from the vanishing gradient problem.
- ResNet achieves this through the use of residual connections, which allow the model to learn residual functions.
- The ResNet model is employed to analyze medical imaging data, such as MRI or CT scans, to predict the presence of Alzheimer’s disease.
DISADVANTAGES
- High Computational Complexity:
- ResNet, especially deeper versions like ResNet-101, requires significant computational resources for training and inference, making it less feasible for deployment on low-resource devices.
- Model Size:
- The ResNet model, due to its deep architecture, has a large number of parameters. This results in longer training times and increased storage requirements, making it less efficient for mobile or embedded system applications.
- Interpretability:
- Despite its high accuracy, deep networks like ResNet can often lack interpretability, making it difficult for clinicians to understand how the model arrives at a diagnosis.
- Limited Flexibility:
- ResNets are less flexible in capturing complex patterns in data compared to some other machine learning models, such as neural networks, which might limit their effectiveness in complex, multi-dimensional problems like career recommendations.
PROPOSED SYSTEM
- The proposed system aims to predict Alzheimer’s disease at an early stage using the MobileNet deep learning algorithm. MobileNet, a lightweight convolutional neural network (CNN) architecture, is chosen for its computational efficiency and ability to deliver accurate results while operating on resource-constrained devices.
- The system will process medical imaging data, such as MRI or CT scans, to identify patterns indicative of Alzheimer’s disease.
- The process begins with data collection and preprocessing, where medical images are resized, normalized, and augmented to enhance model robustness.
- The MobileNet model will be pre-trained on a general image dataset and then fine-tuned using the Alzheimer’s imaging data for classification.
- By extracting features from medical images, the model will classify the data into categories such as Normal, Mild Cognitive Impairment (MCI), or Alzheimer’s Disease.
- For system deployment, the model will be optimized for use on mobile and embedded devices, ensuring accessibility for clinicians who may not have access to high-end computing resources.
- A user-friendly interface will allow clinicians to upload medical images and receive predictions with high accuracy.
- This system will be evaluated using standard performance metrics, including accuracy, precision, recall, and F1-score, to ensure its effectiveness in real-world clinical settings. By integrating deep learning into the diagnostic process, this system provides a scalable, efficient, and cost-effective solution for the early detection of Alzheimer’s disease, supporting timely interventions and improving patient outcomes.
ADVANTAGES
- Lightweight and Efficient:
- MobileNet is designed to be computationally efficient, making it ideal for running on devices with limited resources, such as smartphones, embedded systems, and edge devices. This allows the system to be deployed in real-world healthcare environments without requiring expensive hardware.
- High Performance with Low Latency:
- MobileNet provides high accuracy in image classification tasks while maintaining low latency, ensuring that the model can process and analyze medical images quickly. This enables timely diagnosis and quick decision-making, which is crucial in healthcare, especially for early detection of Alzheimer’s disease.
- Transfer Learning:
- MobileNet allows for transfer learning, where the model is pre-trained on large datasets (e.g., ImageNet) and fine-tuned on specific datasets, such as those for Alzheimer’s disease. This reduces the need for large datasets and extensive training time, improving the overall development process.
PROJECT DEMO VIDEO
HARDWARE REQUIREMENTS:
- System : Intel i3 Processor Mimimum.
- Hard Disk : 20 GB Space
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 4 GB
SOFTWARE REQUIREMENTS:
- Operating System: Windows, Mac OS.
- Coding Language : Python, HTML, CSS,JS
- Web Framework : Flask
Including Packages
=======================
- * Base Paper
- * Complete Source Code
- * Complete Documentation
- * Complete Presentation Slides
- * Flow Diagram
- * Database File
- * Screenshots
- * Execution Procedure
- * Readme File
- * Addons
- * Video Tutorials
- * Supporting Softwares
Specialization =======================
- * Voice Conference
- * Video On Demand
- * Remote Connectivity
- * Code Customization
- * Document Customization
- * Live Chat Support
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