Pneumonia detection from lung X‐ray images using local search aided sine cosine algorithm based deep feature selection method

International Journal of Intelligent SystemsVolume 37, Issue 7 p. 3777-3814 RESEARCH ARTICLE Pneumonia detection from lung X-ray images using local search aided sine cosine algorithm based deep feature selection method Soumitri Chattopadhyay, Soumitri Chattopadhyay orcid.org/0000-0002-2647-6053 Department of Information Technology, Jadavpur University, Kolkata, IndiaSearch for more papers by this authorRohit Kundu, Rohit Kundu Department of Electrical Engineering, Jadavpur University, Kolkata, IndiaSearch for more papers by this authorPawan Kumar Singh, Pawan Kumar Singh orcid.org/0000-0002-9598-7981 Department of Information Technology, Jadavpur University, Kolkata, IndiaSearch for more papers by this authorSeyedali Mirjalili, Corresponding Author Seyedali Mirjalili ali.mirjalili@gmail.com orcid.org/0000-0002-1443-9458 Centre for Artificial Intelligence Research and Optimization, Torrens University, Fortitude Valley, Queensland, Australia Yonser Frontier Lab, Yonsei University, Seoul, Korea Correspondence Seyedali Mirjalili, Centre for Artificial Intelligence Research and Optimization, Torrens University, 90 Bowen Terrace, Fortitude Valley, QLD 4006, Australia. Email: ali.mirjalili@gmail.comSearch for more papers by this authorRam Sarkar, Ram Sarkar orcid.org/0000-0001-8813-4086 Department of Computer Science and Engineering, Jadavpur University, Kolkata, IndiaSearch for more papers by this author Soumitri Chattopadhyay, Soumitri Chattopadhyay orcid.org/0000-0002-2647-6053 Department of Information Technology, Jadavpur University, Kolkata, IndiaSearch for more papers by this authorRohit Kundu, Rohit Kundu Department of Electrical Engineering, Jadavpur University, Kolkata, IndiaSearch for more papers by this authorPawan Kumar Singh, Pawan Kumar Singh orcid.org/0000-0002-9598-7981 Department of Information Technology, Jadavpur University, Kolkata, IndiaSearch for more papers by this authorSeyedali Mirjalili, Corresponding Author Seyedali Mirjalili ali.mirjalili@gmail.com orcid.org/0000-0002-1443-9458 Centre for Artificial Intelligence Research and Optimization, Torrens University, Fortitude Valley, Queensland, Australia Yonser Frontier Lab, Yonsei University, Seoul, Korea Correspondence Seyedali Mirjalili, Centre for Artificial Intelligence Research and Optimization, Torrens University, 90 Bowen Terrace, Fortitude Valley, QLD 4006, Australia. Email: ali.mirjalili@gmail.comSearch for more papers by this authorRam Sarkar, Ram Sarkar orcid.org/0000-0001-8813-4086 Department of Computer Science and Engineering, Jadavpur University, Kolkata, IndiaSearch for more papers by this author First published: 11 October 2021 https://doi.org/10.1002/int.22703Citations: 3Read the full textAboutRelatedInformationPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessClose modalShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract Pneumonia is a major cause of death among children below the age of 5 years, globally. It is especially prevalent in developing and underdeveloped nations where the risk factors for the disease such as unhygienic living conditions, high levels of pollution and overcrowding are higher. Radiological examination (usually X-ray scans) is conducted to detect pneumonia, yet it is prone to subjective variability and can lead to disagreements among different radiologists. To detect traces of pneumonia from X-ray images, a more robust method is therefore required, which can be achieved by using a computer-aided diagnosis (CAD) system. In this study, we develop a two-stage framework, using the combination of deep learning and optimization algorithms, which is both accurate and time-efficient. In its first stage, the proposed framework extracts feature using a customized deep learning model called DenseNet-201 following the concept of transfer learning to cope with the scanty available data. In the second stage, we then reduce the feature dimension using an improved sine cosine algorithm equipped with adaptive beta hill climbing-based local search algorithm. The optimized feature subset is utilized for the classification of “Pneumonia” and “Normal” X-ray images using a support vector machines classifier. Upon an evaluation on a publicly available data set, the proposed method demonstrates the highest accuracy of 98.36% and sensitivity of 98.79% with a feature reduction of 85.55% (74 features selected out of 512), using a five-fold cross-validation scheme. Extensive additional experiments on continuous benchmark functions as well as the CEC-2017 test suite further showcase the superiority and suitability of our proposed approach in application to real-valued optimization problems. The relevant codes for the proposed method can be found in https://github.com/soumitri2001/Pneumonia-Detection-Local-Search-aided-SCA. CONFLICT OF INTERESTS The authors declare that there are no conflict of interests. Citing Literature Volume37, Issue7July 2022Pages 3777-3814 RelatedInformation RecommendedForest optimization algorithm‐based feature selection using classifier ensembleUsha Moorthy, Usha Devi Gandhi, Computational IntelligenceDeep learning on compressed sensing measurements in pneumonia detectionSheikh Rafiul Islam, Santi P. Maity, Ajoy Kumar Ray, Mrinal Mandal, International Journal of Imaging Systems and TechnologyAn OpenCL‐accelerated parallel immunodominance clone selection algorithm for feature selectionHuming Zhu, Yanfei Wu, Pei Li, Peng Zhang, Zhe Ji, Maoguo Gong, Concurrency and Computation: Practice and ExperienceFusion of convolutional neural networks based on Dempster–Shafer theory for automatic pneumonia detection from chest X‐ray imagesSafa Ben Atitallah, Maha Driss, Wadii Boulila, Anis Koubaa, Henda Ben Ghézala, International Journal of Imaging Systems and TechnologyA cost-sensitive deep learning-based meta-classifier for pediatric pneumonia classification using chest X-raysVinayakumar Ravi, Harini Narasimhan, Tuan D. Pham, Expert Systems