A review of machine learning techniques for diagnosing Alzheimer’s disease using imaging modalities

Abstract

Alzheimer's disease is a progressive form of dementia. Dementia is a broad term for conditions that impair memory, thinking, and behaviour. Brain traumas or disorders can cause dementia. It is estimated that 60–80% of dementia cases around the world are caused by Alzheimer’s disease, an incurable neurodegenerative disorder. Although Alzheimer's disease research has increased in recent years, early diagnosis is challenging due to the complicated brain structure and functions associated with this disease. It is difficult for doctors to identify Alzheimer's disease in its early stages as there are still no biomarkers to be precise in early detection. In the area of medical imaging, deep learning is becoming increasingly popular and successful. There is no single best approach for the detection of Alzheimer's disease. In comparison with conventional machine learning methods, the deep learning models detect Alzheimer's disease more precisely and effectively. In this review paper, various machine learning-based techniques utilized for the classification of Alzheimer's disease through different imaging modalities are discussed. In addition, a comprehensive and detailed analysis of the various image processing procedures along with corresponding classification performance and feature extraction techniques have been meticulously compiled and presented. The investigation of computer-aided image analysis has demonstrated significant potential in the early detection of cognitive changes in individuals experiencing mild cognitive impairment. Machine learning can provide valuable insights into the cognitive status of patients, enabling healthcare professionals to intervene and provide timely treatment. This review may lead to a reliable method for recognizing and predicting Alzheimer's disease.