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Experimental results on the chest X-ray and the COVID-19 datasets show that the proposed model can achieve the highest classification rate as compared against the existing models. Moreover, it can significantly reduce the computational parameters of the existing models by 97%. The advantage makes the developed model more attractive than others to deploy in the internet and other device platforms. <\/jats:p>","DOI":"10.1142\/s0218001421570068","type":"journal-article","created":{"date-parts":[[2021,4,14]],"date-time":"2021-04-14T09:13:39Z","timestamp":1618391619000},"page":"2157006","source":"Crossref","is-referenced-by-count":5,"title":["Classification of Chest X-Ray Images Using Novel Adaptive Morphological Neural Networks"],"prefix":"10.1142","volume":"35","author":[{"given":"Shaobo","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA"}]},{"given":"Frank Y.","family":"Shih","sequence":"additional","affiliation":[{"name":"Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA"},{"name":"Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan"}]},{"given":"Xin","family":"Zhong","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Nebraska Omaha, Omaha, NE 68182, USA"}]}],"member":"219","published-online":{"date-parts":[[2021,5,14]]},"reference":[{"key":"S0218001421570068BIB001","first-page":"1","volume-title":"Proc. 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