Alzheimer Early Detection CNN MLP in Python Projects
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Alzheimer Early Detection CNN MLP in Python Projects
Abstract
Alzheimer’s disease is a progressive neurodegenerative disorder that impairs memory, cognition, and daily functioning. Early detection of Alzheimer’s is crucial for timely intervention and slowing the progression of symptoms. Traditional diagnosis methods rely on clinical assessments, cognitive tests, and medical imaging, which can be time-consuming and expensive. This project, Alzheimer Early Detection using CNN–MLP in Python, presents a deep learning–based system for identifying early signs of Alzheimer’s from medical imaging data (e.g., MRI scans). The system uses Convolutional Neural Networks (CNNs) for feature extraction and a Multi-Layer Perceptron (MLP) for classification of brain scan images into categories such as normal, mild cognitive impairment (MCI), and Alzheimer’s disease. Developed using Python libraries like TensorFlow/Keras, OpenCV, and Scikit-learn, the model aims to provide an efficient and accurate tool for medical research and early screening support.
Existing System
Current Alzheimer’s detection practices mainly depend on manual analysis of MRI or PET scans and neuropsychological evaluations. These methods require expert radiologists and neurologists, making the process costly, time-intensive, and not widely accessible. Some machine learning methods have been applied, using handcrafted features from brain scans, but they often fail to capture the complex spatial patterns associated with early stages of the disease. Existing systems are also limited in scalability and accuracy, as they struggle with subtle variations between healthy brains and those with mild cognitive impairment. Furthermore, most existing systems are not integrated into real-time, user-friendly platforms for broader usability.
Proposed System
The proposed system introduces a hybrid deep learning model combining CNN and MLP for early Alzheimer’s detection. CNN layers are used to extract spatial and structural features from brain MRI images, while the MLP classifier interprets these features and predicts the likelihood of Alzheimer’s at different stages. The system is trained on publicly available medical imaging datasets (e.g., ADNI or Kaggle datasets) and evaluated for accuracy, sensitivity, and specificity. The Python-based model can be extended into a Flask/Django web application where clinicians or researchers upload brain scan images and receive instant predictions. Compared to existing methods, the proposed system is faster, more accurate, and scalable, offering valuable support for healthcare professionals in early diagnosis and treatment planning.
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