Abstract
Artificial neural network (ANN) ability to learn, correct errors, and
transform a large amount of raw data into useful medical decisions for
treatment and care have increased its popularity for enhanced patient
safety and quality of care. Therefore, this paper reviews the critical
role of ANNs in providing valuable insights for patients’ healthcare
decisions and efficient disease diagnosis. We thoroughly review
different types of ANNs presented in the existing literature that
advanced ANNs adaptation for complex applications. Moreover, we also
investigate ANN’s advances for various disease diagnoses and treatments
such as viral, skin, cancer, and COVID-19. Furthermore, we propose a
novel deep Convolutional Neural Network (CNN) model called ConXNet for
improving the detection accuracy of COVID-19 disease. ConXNet is trained
and tested using different datasets, and it achieves more than 97%
detection accuracy and precision, which is significantly better than
existing models. Finally, we highlight future research directions and
challenges such as complexity of the algorithms, insufficient available
data, privacy and security, and integration of biosensing with ANNs.
These research directions require considerable attention for improving
the scope of ANNs for medical diagnostic and treatment applications.