{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T21:37:58Z","timestamp":1791236278240,"version":"4.1.0"},"reference-count":38,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T00:00:00Z","timestamp":1682553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Trade, Industry &amp; Energy, Republic of Korea","award":["20204010600090"],"award-info":[{"award-number":["20204010600090"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Leukocytes, also referred to as white blood cells (WBCs), are a crucial component of the human immune system. Abnormal proliferation of leukocytes in the bone marrow leads to leukemia, a fatal blood cancer. Classification of various subtypes of WBCs is an important step in the diagnosis of leukemia. The method of automated classification of WBCs using deep convolutional neural networks is promising to achieve a significant level of accuracy, but suffers from high computational costs due to very large feature sets. Dimensionality reduction through intelligent feature selection is essential to improve the model performance with reduced computational complexity. This work proposed an improved pipeline for subtype classification of WBCs that relies on transfer learning for feature extraction using deep neural networks, followed by a wrapper feature selection approach based on a customized quantum-inspired evolutionary algorithm (QIEA). This algorithm, inspired by the principles of quantum physics, outperforms classical evolutionary algorithms in the exploration of search space. The reduced feature vector obtained from QIEA was then classified with multiple baseline classifiers. In order to validate the proposed methodology, a public dataset of 5000 images of five subtypes of WBCs was used. The proposed system achieves a classification accuracy of about 99% with a reduction of 90% in the size of the feature vector. The proposed feature selection method also shows a better convergence performance as compared to the classical genetic algorithm and a comparable performance to several existing works.<\/jats:p>","DOI":"10.3390\/cancers15092507","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T01:33:40Z","timestamp":1682645620000},"page":"2507","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Leukocytes Classification for Leukemia Detection Using Quantum Inspired Deep Feature Selection"],"prefix":"10.3390","volume":"15","author":[{"given":"Riaz","family":"Ahmad","sequence":"first","affiliation":[{"name":"Department of Computer Science, Iqra University, Islamabad 44800, Pakistan"},{"name":"Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah 47010, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3791-4140","authenticated-orcid":false,"given":"Muhammad","family":"Awais","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, COMSATS University Islamabad, Wah Campus, Wah 47010, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1705-5027","authenticated-orcid":false,"given":"Nabeela","family":"Kausar","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Iqra University, Islamabad 44800, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7672-1187","authenticated-orcid":false,"given":"Usman","family":"Tariq","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae-Hyuk","family":"Cha","sequence":"additional","affiliation":[{"name":"Department of computer Science, Hanyang University, Seoul 04763, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jamel","family":"Balili","sequence":"additional","affiliation":[{"name":"College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.cbi.2017.11.007","article-title":"Erythrocytes as a biological model for screening of xenobiotics toxicity","volume":"279","author":"Farag","year":"2018","journal-title":"Chem. 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