Alzheimer Disease Detection and Stages Classification

Project Domain / Category

Image Processing / Artificial Intelligence / Web App

Abstract / Introduction

Alzheimer's disease is a brain disorder that affects memory, thinking, and behavior. It is the most common cause of dementia, which is a decline in cognitive function severe enough to interfere with daily life. Alzheimer's gradually worsens over time, and it eventually becomes difficult for affected individuals to carry out simple tasks. Alzheimer's disease typically progresses through stages, starting with mild memory loss and confusion, then advancing to severe memory impairment, personality changes, and difficulties with basic tasks like dressing or eating. In the later stages, individuals may require a caretaker all the time. Alzheimer’s disease has six different stages which are as follows: (i) control normal (CN) is the first stage where no symptoms of the disease are shown. (ii) Significant memory concern (SMC) is the next stage, which is characterized by minor memory related issues that are difficult to detect and are like normal age-related problems. (iii) Early mild cognitive impairment (EMCI) stage causes difficulty in arranging items and planning new things. (iv) The distinguishable symptoms of the disease become visible in the fourth stage called mild cognitive impairment (MCI) stage. Here, the patient is having trouble solving simple math-related problems or managing financial tasks. (v) In the late mild cognitive impairment (LMCI) stage, the person experiences problems remembering details. They need help from their guardians to manage their daily tasks. The patients feel difficulty in their surroundings. (vi) In the last stage of Alzheimer’s disease (AD), the person becomes unable to interact with his environment. The last stage often results in a patient’s death. The conversion from one stage of AD to another depends on the patient’s condition. The project includes detecting Alzheimer disease from a patient MRI scan and classifying it into one of the mentioned stages. You are required to develop a web app in which the patient will enter his/her MRI scan and check his/her status.

Functional Requirements:

         Dataset Collection: Collect MRI or FMRI related dataset from available free repositories or any other online source.

         Pre-Processing: The collected dataset contains MRI or FMRI scans. Convert these into images using python and use different image processing techniques to create a uniform, normalized image dataset. You may need to perform data augmentation in this step.

         Model Selection: Analyze different deep learning-based CNN models and select a suitable one for Alzheimer disease classification.

         Dataset Splitting: Split the dataset into training and testing set for model evaluation.

         Model Training: Train the selected model using training dataset.

            Validation and Hyperparameter Tuning: Validate the model's performance using the validation set and fine-tune hyperparameters like learning rate, batch size, and network architecture to achieve the best results.

            Model Evaluation: Check the performance of the model used using testing dataset and different evaluation metrics.

            Real-time Detection: Implement a real-time AD detection pipeline using OpenCV to upload an image from and apply the trained model for AD stage detection.