# Neural Networks for Infectious Diseases Detection: Prospects and Challenges

arXiv: https://arxiv.org/abs/2112.03571 · HTML: https://mlsanity.com/paper/2112.03571/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 144 references that are in the map.
- First posted on arXiv 7 Dec 2021.

## Builds on (most cited first, 21 of 21)

- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [State-of-the-art Speech Recognition With Sequence-to-Sequence Models](https://mlsanity.com/paper/1712.01769/) (2017)
- [Deep neural network models for computational histopathology: A survey](https://mlsanity.com/paper/1912.12378/) (2019)
- [Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation](https://mlsanity.com/paper/1904.07773/) (2019)
- [A Survey of Optimization Methods from a Machine Learning Perspective](https://mlsanity.com/paper/1906.06821/) (2019)
- [CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images](https://mlsanity.com/paper/2004.04931/) (2020)
- [Survey of Dropout Methods for Deep Neural Networks](https://mlsanity.com/paper/1904.13310/) (2019)
- [Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network](https://mlsanity.com/paper/2003.13815/) (2020)
- [Pruning Algorithms to Accelerate Convolutional Neural Networks for Edge Applications: A Survey](https://mlsanity.com/paper/2005.04275/) (2020)
- [Prostate Cancer Detection using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1905.13145/) (2019)
- [COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches](https://mlsanity.com/paper/2004.03747/) (2020)
- [A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19](https://mlsanity.com/paper/2007.02202/) (2020)
- [Computer Vision For COVID-19 Control: A Survey](https://mlsanity.com/paper/2004.09420/) (2020)
- [PDCOVIDNet: A Parallel-Dilated Convolutional Neural Network Architecture for Detecting COVID-19 from Chest X-Ray Images](https://mlsanity.com/paper/2007.14777/) (2020)
- [Theoretical Analysis of the k-Means Algorithm - A Survey](https://mlsanity.com/paper/1602.08254/) (2016)
- [Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model](https://mlsanity.com/paper/1904.11576/) (2019)
- [Stacked Convolutional Neural Network for Diagnosis of COVID-19 Disease from X-ray Images](https://mlsanity.com/paper/2006.13817/) (2020)
- [An Ensemble of Deep Convolutional Neural Networks for Alzheimer's Disease Detection and Classification](https://mlsanity.com/paper/1712.01675/) (2017)
- [Reducing Artificial Neural Network Complexity: A Case Study on Exoplanet Detection](https://mlsanity.com/paper/1902.10385/) (2019)
- [Industrial Scale Privacy Preserving Deep Neural Network](https://mlsanity.com/paper/2003.05198/) (2020)

## Abstract (arXiv)

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.