Dementia Classification Using Deep Reinforcement Learning for Early Diagnosis

• Published in: Applied sciences 2023-01, Vol.13 (3), p.1464
• Main Authors: Hashmi, Arshad, Barukab, Omar
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Alzheimer; Alzheimer's disease; Artificial intelligence; Brain research; Classification; Cognitive ability; Deep learning; Dementia; Dementia disorders; Diagnosis; Illnesses; Image retrieval; magnetic Imaging resonance; Magnetic resonance imaging; Neural networks; Neuroimaging; Patients; Population growth; Reinforcement; reinforcement learning
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13031464

Neurodegeneration and impaired neuronal transmission in the brain are at the root of Alzheimer’s disease (AD) and dementia. As of yet, no successful treatments for dementia or Alzheimer’s disease have indeed been found. Therefore, preventative measures such as early diagnosis are essential. This research aimed to evaluate the accuracy of the Open Access Series of Imaging Studies (OASIS) database for the purpose of identifying biomarkers of dementia using effective machine learning methods. In most parts of the world, AD is responsible for dementia. When the challenge level is high, it is nearly impossible to get anything done without assistance. This is increasing due to population growth and the diagnostic period. Two current approaches are the medical history and testing. The main challenge for dementia research is the imbalance of datasets and their impact on accuracy. A proposed system based on reinforcement learning and neural networks could generate and segment imbalanced classes. Making a precise diagnosis and taking into account dementia in all four stages will result in high-resolution sickness probability maps. It employs deep reinforcement learning to generate accurate and understandable representations of a person’s dementia sickness risk. To avoid an imbalance, classes should be evenly represented in the samples. There is a significant class imbalance in the MRI image. The Deep Reinforcement System improved trial accuracy by 6%, precision by 9%, recall by 13%, and F-score by 9–10%. The diagnosis efficiency has improved as well.