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An adaptive and altruistic PSO-based deep feature selection method for Pneumonia detection from Chest X-rays

Pneumonia is one of the major reasons for child mortality especially in income-deprived regions of the world. Although it can be detected and treated with very less sophisticated instruments and medication, Pneumonia detection still remains a major concern in developing countries. Computer-aided bas...

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Detalles Bibliográficos
Autores principales: Pramanik, Rishav, Sarkar, Sourodip, Sarkar, Ram
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9364947/
https://www.ncbi.nlm.nih.gov/pubmed/35966452
http://dx.doi.org/10.1016/j.asoc.2022.109464
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author Pramanik, Rishav
Sarkar, Sourodip
Sarkar, Ram
author_facet Pramanik, Rishav
Sarkar, Sourodip
Sarkar, Ram
author_sort Pramanik, Rishav
collection PubMed
description Pneumonia is one of the major reasons for child mortality especially in income-deprived regions of the world. Although it can be detected and treated with very less sophisticated instruments and medication, Pneumonia detection still remains a major concern in developing countries. Computer-aided based diagnosis (CAD) systems can be used in such countries due to their lower operating costs than professional medical experts. In this paper, we propose a CAD system for Pneumonia detection from Chest X-rays, using the concepts of deep learning and a meta-heuristic algorithm. We first extract deep features from the pre-trained ResNet50, fine-tuned on a target Pneumonia dataset. Then, we propose a feature selection technique based on particle swarm optimization (PSO), which is modified using a memory-based adaptation parameter, and enriched by incorporating an altruistic behavior into the agents. We name our feature selection method as adaptive and altruistic PSO (AAPSO). The proposed method successfully eliminates non-informative features obtained from the ResNet50 model, thereby improving the Pneumonia detection ability of the overall framework. Extensive experimentation and thorough analysis on a publicly available Pneumonia dataset establish the superiority of the proposed method over several other frameworks used for Pneumonia detection. Apart from Pneumonia detection, AAPSO is further evaluated on some standard UCI datasets, gene expression datasets for cancer prediction and a COVID-19 prediction dataset. The overall results are satisfactory, thereby confirming the usefulness of AAPSO in dealing with varied real-life problems. The supporting source codes of this work can be found at https://github.com/rishavpramanik/AAPSO.
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spelling pubmed-93649472022-08-10 An adaptive and altruistic PSO-based deep feature selection method for Pneumonia detection from Chest X-rays Pramanik, Rishav Sarkar, Sourodip Sarkar, Ram Appl Soft Comput Article Pneumonia is one of the major reasons for child mortality especially in income-deprived regions of the world. Although it can be detected and treated with very less sophisticated instruments and medication, Pneumonia detection still remains a major concern in developing countries. Computer-aided based diagnosis (CAD) systems can be used in such countries due to their lower operating costs than professional medical experts. In this paper, we propose a CAD system for Pneumonia detection from Chest X-rays, using the concepts of deep learning and a meta-heuristic algorithm. We first extract deep features from the pre-trained ResNet50, fine-tuned on a target Pneumonia dataset. Then, we propose a feature selection technique based on particle swarm optimization (PSO), which is modified using a memory-based adaptation parameter, and enriched by incorporating an altruistic behavior into the agents. We name our feature selection method as adaptive and altruistic PSO (AAPSO). The proposed method successfully eliminates non-informative features obtained from the ResNet50 model, thereby improving the Pneumonia detection ability of the overall framework. Extensive experimentation and thorough analysis on a publicly available Pneumonia dataset establish the superiority of the proposed method over several other frameworks used for Pneumonia detection. Apart from Pneumonia detection, AAPSO is further evaluated on some standard UCI datasets, gene expression datasets for cancer prediction and a COVID-19 prediction dataset. The overall results are satisfactory, thereby confirming the usefulness of AAPSO in dealing with varied real-life problems. The supporting source codes of this work can be found at https://github.com/rishavpramanik/AAPSO. Elsevier B.V. 2022-10 2022-08-10 /pmc/articles/PMC9364947/ /pubmed/35966452 http://dx.doi.org/10.1016/j.asoc.2022.109464 Text en © 2022 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Pramanik, Rishav
Sarkar, Sourodip
Sarkar, Ram
An adaptive and altruistic PSO-based deep feature selection method for Pneumonia detection from Chest X-rays
title An adaptive and altruistic PSO-based deep feature selection method for Pneumonia detection from Chest X-rays
title_full An adaptive and altruistic PSO-based deep feature selection method for Pneumonia detection from Chest X-rays
title_fullStr An adaptive and altruistic PSO-based deep feature selection method for Pneumonia detection from Chest X-rays
title_full_unstemmed An adaptive and altruistic PSO-based deep feature selection method for Pneumonia detection from Chest X-rays
title_short An adaptive and altruistic PSO-based deep feature selection method for Pneumonia detection from Chest X-rays
title_sort adaptive and altruistic pso-based deep feature selection method for pneumonia detection from chest x-rays
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9364947/
https://www.ncbi.nlm.nih.gov/pubmed/35966452
http://dx.doi.org/10.1016/j.asoc.2022.109464
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