The main objective of this project is to develop an intelligent system that can recognize and categorize the four stages of AD (Alzheimer’s Disease) utilizing cutting-edge image analysis methods and deep learning models, as well as to provide a full report on the model’s diagnosis of the patient. This research will use MRI(Magnetic resonance imaging), which has been determined to be one of the most prevalent and common medical imaging methods, taking a non-invasive approach in the identification and classification of the AD stages. As such, we will utilize SHAP and a fuzzy-based expert system to fulfill the full potential for each diagnosis moreover, and we will choose the most efficient deep learning classifier to employ in the feature extraction phase.

• Fully automated diagnosis and discrimination between the stages of the disease while also being able to declare the mentioned stages (Preclinical Alzheimer’s, Mild cognitive impairment, Mild dementia, Moderate dementia, and Severe dementia)

• Providing a fully comprehensive explanation of each feature given out in the result using fuzzy and shap Machine learning rules.

• Optimizing the automatic selection of the many features provided.

• Increasing the performance and efficiency of our model compared to previous research works

The system uses a brain MRI image to detect and diagnose the four stages of Alzheimer’s disease (Preclinical Alzheimer’s, Mild cognitive impairment, Mild dementia, Moderate dementia, and Severe dementia). The classification system will detect the abnormality faster than a traditional diagnosis while also being able to detect Alzheimer’s in its earlier stages. The system will:

• Classify whether the brain’s MRI is healthy or demented.

• Classify the stage of the disease (Preclinical Alzheimer’s, Mild cognitive impairment, Mild dementia, Moderate dementia, and Severe dementia).

• Generate a patient report.

• Generate a report with a full description of the reasoning behind each choice the model made