Neural Networks for Infectious Diseases Detection: Prospects and Challenges

TL;DR

This paper reviews the use of artificial neural networks in disease diagnosis and introduces ConXNet, a novel CNN model that achieves over 97% accuracy in COVID-19 detection, highlighting future challenges and research directions.

Contribution

It presents a comprehensive review of ANN applications in healthcare and introduces ConXNet, a new deep CNN model that significantly improves COVID-19 detection accuracy.

Findings

ConXNet achieves over 97% accuracy in COVID-19 detection.

ANNs are effective in diagnosing various diseases including COVID-19.

Future challenges include data scarcity, privacy issues, and algorithm complexity.

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.