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ABSTRACT
There are many different causes of dementia, but Alzheimer’s Disease is the most
common form. As the condition progresses, it limits one’s ability to perform any task
without aid, and the diagnosis timeline and ageing population are expected to cause its
prevalence to increase. The conventional ways of detecting Alzheimer’s are tiring for
both patients and doctors where it involves retrieving the past medical records and having
Magnetic Resonance Imaging scans and even neurophysical testing which can be
inconvenient for patients. An early diagnosis of brain diseases makes a big difference
when it comes to attempting to cure them. Our work has used deep learning (neural
networks) to detect Alzheimer’s disease earlier than usual by combining it with deep
learning. As the obtained dataset from Kaggle is heavily imbalanced, we evenly
distributed the data between the categories using SMOTE. Then the model is trained
and tested with the categorized MRI data i.e. very mild, mild, moderate and severe AD
and finally features to examine the results. The results we achieved are
compared with the previous attempts at the detection of Alzheimer’s and came out to be
significantly greater in terms of precision and accuracy.
Abstract
The project “Early Diagnosis of Alzheimer’s Disease Using Deep Learning” aims to develop an advanced system for the early detection of Alzheimer’s disease through the application of deep learning techniques. Leveraging Python and web technologies, this project introduces a novel approach to analysing medical imaging data, providing a potential breakthrough in the early diagnosis of Alzheimer’s disease.
Existing System on early diagnosis of alzheimers disease
The existing methods for Alzheimer’s diagnosis often rely on traditional clinical assessments, which may not be sensitive enough to detect early-stage symptoms. Current diagnostic processes lack the efficiency and accuracy required for timely intervention and treatment.
Proposed System
The proposed system utilizes deep learning algorithms to analyze neuroimaging data, aiming for early detection of Alzheimer’s disease. It incorporates a user-friendly web interface to facilitate the interaction between medical professionals and the system, enhancing the diagnostic process and potentially improving patient outcomes.
Detailed Module-Wise Explanation
1. Data Collection Moduleon early diagnosis of alzheimers disease
– Acquires neuroimaging data, such as MRI scans, from patients.
– Organizes and preprocesses the data for model training.
2. Deep Learning Model Training Module on early diagnosis of alzheimers disease
– Utilizes convolutional neural networks (CNNs) for feature extraction.
– Trains the model on a labelled dataset containing both healthy and Alzheimer ‘s-affected brain images.
3. Diagnostic Moduleon early diagnosis of alzheimers disease
– Processes new neuroimaging data through the trained model.
– Generates diagnostic reports indicating the likelihood of Alzheimer’s disease.
4. Web Interface Module on early diagnosis of alzheimers disease
– Provides a user-friendly dashboard for medical professionals.
– Allows for uploading new patient data and viewing diagnostic results.
System Requirements on early diagnosis of alzheimers disease
Hardware Requirements
– High-performance computing infrastructure for deep learning model training.
– Standard computer systems for web-based access.
Software Requirements
– Python for backend development and deep learning model implementation.
– Web development frameworks (e.g., Django) for creating the user interface.
– Deep learning libraries (e.g., TensorFlow, PyTorch) for model training.
– Database management system (e.g., SQLite) for storing patient data.
Algorithms on early diagnosis of alzheimers disease:
The project employs convolutional neural networks (CNNs) for feature extraction from neuroimaging data. The deep learning model is trained using supervised learning techniques on a labelled dataset containing both healthy and Alzheimer ‘s-affected brain images.
Hardware and Software Requirements:
The system requires high-performance computing resources for model training, including GPUs for accelerated deep-learning computations. On the software side, Python serves as the primary programming language, with frameworks such as TensorFlow or PyTorch for deep learning, Django for web development, and SQLite for database management.
Architecture on early diagnosis of alzheimers disease:
The system follows a modular architecture with distinct components for data collection, model training, diagnosis, and the web interface. A backend server handles data processing and model-related computations, while the web interface provides a seamless interaction platform for medical professionals.
Technologies Used on early diagnosis of alzheimers disease
-Python
Primary programming language for backend development and deep learning implementation.
-Django
Web development framework for creating an interactive and user-friendly interface.
– TensorFlow/PyTorch:
Deep learning libraries for implementing and training the CNN model.
– SQLite:
Database management system for storing patient data.
Web User Interface on early diagnosis of alzheimers disease
The web interface is designed to be intuitive and accessible to medical professionals. It allows for the easy upload of patient neuroimaging data, provides real-time diagnostic results, and offers a platform for collaboration among healthcare professionals involved in Alzheimer’s disease diagnosis and treatment. The interface prioritizes user experience and ensures seamless navigation through the various functionalities of the system.
UML DIAGRAMS on early diagnosis of alzheimers disease
