# Optimized Deep Feature Selection for Pneumonia Detection: A Novel RegNet and XOR-Based PSO Approach

arXiv: https://arxiv.org/abs/2309.00147 · HTML: https://mlsanity.com/paper/2309.00147/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [RegNet: Self-Regulated Network for Image Classification](https://mlsanity.com/paper/2101.00590/) (2021)
- [An Adaptive and Altruistic PSO-based Deep Feature Selection Method for Pneumonia Detection from Chest X-Rays](https://mlsanity.com/paper/2208.03558/) (2022)

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

- [From Blind Solvers to Logical Thinkers: Benchmarking LLMs' Logical Integrity on Faulty Mathematical Problems](https://mlsanity.com/paper/2410.18921/) (2024)

## Abstract (arXiv)

Pneumonia remains a significant cause of child mortality, particularly in developing countries where resources and expertise are limited. The automated detection of Pneumonia can greatly assist in addressing this challenge. In this research, an XOR based Particle Swarm Optimization (PSO) is proposed to select deep features from the second last layer of a RegNet model, aiming to improve the accuracy of the CNN model on Pneumonia detection. The proposed XOR PSO algorithm offers simplicity by incorporating just one hyperparameter for initialization, and each iteration requires minimal computation time. Moreover, it achieves a balance between exploration and exploitation, leading to convergence on a suitable solution. By extracting 163 features, an impressive accuracy level of 98% was attained which demonstrates comparable accuracy to previous PSO-based methods. The source code of the proposed method is available in the GitHub repository.