Applying Multimodal Data Fusion based on Deep Learning Methods for the Diagnosis of Neglected Tropical Diseases: A Systematic Review

Abstract

Abstract Neglected tropical diseases (NTDs) are the most prevalent diseases worldwide, affecting one-tenth of the world population. Although there are multiple approaches to diagnosing these diseases, using skin manifestations and lesions caused as a result of these diseases along with other medical records is the preferred method. This fact triggers the need to explore and implement a deep learning-based diagnostic model using multimodal data fusion (MMDF) techniques to enhance the diagnostic process. This paper, thus, endeavors to present a thorough systematic review of studies regarding the implementation of MMDF techniques for the diagnosis of skin-related NTDs. To achieve its objective, the study used the PRISMA method based on predefined questions and collected 427 articles from seven major and reputed sources and critically appraised each article. Since no previous studies were found regarding the implementation of MMDF for the diagnoses of skin related NTDs, similar studies using MMDF for the diagnoses of other skin diseases, such as skin cancer, were collected and analyzed in this review to extract information about the implementation of these methods. In doing so, various studies are analyzed using six different parameters, including research approaches, disease selected for diagnosis, dataset, algorithms, performance achievements, and future directions. Accordingly, although all the studies used diverse research methods and datasets based on their problems, deep learning-based convolutional neural networks (CNN) algorithms are found to be the most frequently used and best-performing models in all the studies reviewed.