# Fitness Dependent Optimizer with Neural Networks for COVID-19 patients

arXiv: https://arxiv.org/abs/2302.02986 · HTML: https://mlsanity.com/paper/2302.02986/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 58 references that are in the map.
- First posted on arXiv 6 Jan 2023.

## Builds on (most cited first, 11 of 11)

- [Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks](https://mlsanity.com/paper/2003.11617/) (2020)
- [Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2003.10849/) (2020)
- [CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images](https://mlsanity.com/paper/2004.04931/) (2020)
- [Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network](https://mlsanity.com/paper/2003.13815/) (2020)
- [COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs](https://mlsanity.com/paper/2003.14395/) (2020)
- [A Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-Rays](https://mlsanity.com/paper/2005.01578/) (2020)
- [Neural network based country wise risk prediction of COVID-19](https://mlsanity.com/paper/2004.00959/) (2020)
- [Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process](https://mlsanity.com/paper/1904.05226/) (2019)
- [Real-Time COVID-19 Diagnosis from X-Ray Images Using Deep CNN and Extreme Learning Machines Stabilized by Chimp Optimization Algorithm](https://mlsanity.com/paper/2106.01435/) (2021)
- [A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer](https://mlsanity.com/paper/1903.11712/) (2019)
- [Improved Fitness-Dependent Optimizer Algorithm](https://mlsanity.com/paper/2001.11820/) (2020)

## Built on by (most cited first, 1 of 1)

- [Comparative Analysis of AES, Blowfish, Twofish, Salsa20, and ChaCha20 for Image Encryption](https://mlsanity.com/paper/2407.16274/) (2024)

## Abstract (arXiv)

The Coronavirus, known as COVID-19, which appeared in 2019 in China, has significantly affected global health and become a huge burden on health institutions all over the world. These effects are continuing today. One strategy for limiting the virus's transmission is to have an early diagnosis of suspected cases and take appropriate measures before the disease spreads further. This work aims to diagnose and show the probability of getting infected by the disease according to textual clinical data. In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the best results with 100% accuracy. The other models' results varied from one experiment to the other. It is concluded that the models on which the FDO algorithm was used as a learning algorithm had the possibility of obtaining higher accuracy. However, it is found that FDO has the longest runtime compared to the other algorithms. The link to the covid 19 models is found here: https://github.com/Tarik4Rashid4/covid19models