Deep Learning Algorithms for Detecting Alzheimer’s Disease using WBSN

Brain-related diseases are among the most difficult diseases to cure due to their sensitivity, the danger associated with performing operations, and their unanticipated costs. In contrast, the surgery is not required to be successful, as its effects may be unsuccessful. Therefore, it should be diagnosed at an early stage and the patient should be treated so that they can lead a normal life on their own. WBSN is a novel technology that is applicable in a range of healthcare contexts. WBSNs are made up of several sensors that are implanted in, around, or on the human body to track things like temperature, blood pressure, ECG, and EEG. In developed countries, Alzheimer's disease (AD) is one of the major causes of death. Using computer-aided algorithms has produced outstanding scientific findings, but no practically applicable diagnostic approach is currently available. In recent years, deep models have gained popularity, particularly for image processing. Deep learning methods surpass conventional machine learning algorithms in the recognition of complex patterns in vast quantities of high-dimensional medical imaging data. As a result, there is a growing interest in applying deep learning to medical diagnoses. In this paper, we will examine the most prevalent deep learning algorithms for diagnosing Alzheimer's illness using WBSN.