Enhancing Alzheimer's Disease Diagnosis: The Efficacy of the YOLO Algorithm Model

The diagnosis and early detection of Alzheimer's Disease (AD) and other forms of dementia have become increasingly crucial as our aging population grows. In recent years, deep learning, particularly the You Only Look Once (YOLO) architecture, has emerged as a promising tool in the field of neuroimaging and machine learning for AD diagnosis. This comprehensive review investigates the recent advances in the application of YOLO for AD diagnosis and classification. We scrutinized five research papers that have explored the potential of YOLO, delving into the methodologies, datasets, and results presented. Our review reveals the remarkable strides made in AD diagnosis using YOLO, while also highlighting challenges, such as data scarcity and research lacking. The paper provides insights into the growing role of YOLO in the early detection of AD and its potential to transform clinical practices in the field. This review aims to inspire further research and innovation to enhance AD diagnosis and, ultimately, patient care.

@article{Vinh2023,
  title     = {Enhancing Alzheimer's Disease Diagnosis: The Efficacy of the YOLO Algorithm Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {11},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Tran Quang Vinh and Haewon Byeon},
  doi       = {10.14569/IJACSA.2023.0141182},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141182}
}