{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T20:49:56Z","timestamp":1781815796705,"version":"3.54.5"},"reference-count":61,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T00:00:00Z","timestamp":1594166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Tuberculosis (TB) is is an infectious disease that generally attacks the lungs and causes death for millions of people annually. Chest radiography and deep-learning-based image segmentation techniques can be utilized for TB diagnostics. Convolutional Neural Networks (CNNs) has shown advantages in medical image recognition applications as powerful models to extract informative features from images. Here, we present a novel hybrid method for efficient classification of chest X-ray images. First, the features are extracted from chest X-ray images using MobileNet, a CNN model, which was previously trained on the ImageNet dataset. Then, to determine which of these features are the most relevant, we apply the Artificial Ecosystem-based Optimization (AEO) algorithm as a feature selector. The proposed method is applied to two public benchmark datasets (Shenzhen and Dataset 2) and allows them to achieve high performance and reduced computational time. It selected successfully only the best 25 and 19 (for Shenzhen and Dataset 2, respectively) features out of about 50,000 features extracted with MobileNet, while improving the classification accuracy (90.2% for Shenzen dataset and 94.1% for Dataset 2). The proposed approach outperforms other deep learning methods, while the results are the best compared to other recently published works on both datasets.<\/jats:p>","DOI":"10.3390\/sym12071146","type":"journal-article","created":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T09:45:31Z","timestamp":1594374331000},"page":"1146","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":135,"title":["A Novel Method for Detection of Tuberculosis in Chest Radiographs Using Artificial Ecosystem-Based Optimisation of Deep Neural Network Features"],"prefix":"10.3390","volume":"12","author":[{"given":"Ahmed T.","family":"Sahlol","sequence":"first","affiliation":[{"name":"Computer Department, Faculty of Specific Education, Damietta University, Damietta 34517, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7682-6269","authenticated-orcid":false,"given":"Mohamed","family":"Abd Elaziz","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9725-7985","authenticated-orcid":false,"given":"Amani","family":"Tariq Jamal","sequence":"additional","affiliation":[{"name":"Computer Science Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9990-1084","authenticated-orcid":false,"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[{"name":"Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania"},{"name":"Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8856-9968","authenticated-orcid":false,"given":"Osama","family":"Farouk Hassan","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Science, Damanhour University, Damanhur 22511, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6268","DOI":"10.1038\/s41598-019-42557-4","article-title":"Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization","volume":"9","author":"Pasa","year":"2019","journal-title":"Sci. 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