With the aim of improving the early detection of Alzheimer’s disease, this article focuses on the use of advanced algorithms, such as Deep Learning, to analyse large amounts of image data. To achieve this goal, the article proposes using a Convolutional Neural Network (CNN) model, which is a type of Deep Learning algorithm that excels in image analysis. By training a CNN on a large dataset of medical images, the model can learn to automatically identify patterns and features that are indicative of Alzheimer’s disease. The ultimate goal is to develop a web application that can detect Alzheimer’s disease in its early stages with high accuracy.
In the context of detecting Alzheimer’s disease, Computer Vision, Image Classification and Deep Learning come together by using Convolutional Neural Networks to classify brain MRI scans to predict the presence/absence of Alzheimer’s disease.
The dataset comes from Kaggle and consists of 6400 pre-processed brain MRI images:
Class - 1: Mild Demented (896 images)
Class - 2: Moderate Demented (64 images)
Class - 3: Non Demented (3200 images)
Class - 4: Very Mild Demented (2240 images)
os and cv2 are used for image processing and handling;
matplotlib and seaborn are used for data visualization;
sklearn is used for machine learning algorithms;
tensorflow and keras are used for building and training deep learning models;
streamlit is used for creating web-based applications to visualize and interact with the trained models.
After training and evaluating our model, we deploy it in a Streamlit App that allows users to upload brain MRI scans and obtain predictions on whether the patient has Alzheimer’s disease.
Thank you for your interest in our article "On the early detection of Alzheimer's disease using a Deep Learning approach". You can access the article through the following link, which is published on Medium.com:
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