{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:24:55Z","timestamp":1759537495682,"version":"build-2065373602"},"reference-count":31,"publisher":"World Scientific Pub Co Pte Ltd","issue":"18","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:p> The human respiratory system relies on the lungs for oxygen and carbon dioxide exchange. However, image-processing techniques face challenges in identifying, evaluating and classifying lung disorders. Thus, a novel \u201cEfficient Contrast Multiscale ResNet with Nature-Inspired Boost Optimization Networks\u201d has been proposed. However, variations in texture caused by an uneven distribution of tissue density during preprocessing lead\u00a0to inaccurate tissue characterization and complicate feature extraction by influencing texture analysis methods. Hence, a Multiscale Bee Binary Optimized Network has been introduced for preprocessing and feature extraction, in which Contrast Multiscale Binary pattern is used for preprocessing by reducing the possibility of mischaracterization due to density-induced variations, and Augmented Graph Bee Colony is used for feature extraction to obtain a more accurate depiction of relationships and contextual information. Detecting lung diseases is challenging due to subtle abnormalities and gradual evolution, while classification of radiological patterns in infections, cancer, asthma and other diseases causes confusion and reduced accuracy. So, a novel Optimized Ant ResNet Swarm Boost Network is introduced for detection and classification, in which ResNet swarm R-network is used for detection by learning complex features from medical images, focusing on refining detection criteria, and Elitist Net ANT optimization is used for classification by optimizing informative features and model parameters for enhancing classification accuracy. The proposed method demonstrates excellence in preprocessing, feature extraction, detection and classification, achieving high accuracy and recall. <\/jats:p>","DOI":"10.1142\/s0218126625503815","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T15:23:37Z","timestamp":1752679417000},"source":"Crossref","is-referenced-by-count":0,"title":["Efficient Contrast Multiscale ResNet with Nature-Inspired Boost Optimization Networks for Identifying Lung Diseases"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2446-6932","authenticated-orcid":false,"given":"Sirikonda","family":"Shwetha","sequence":"first","affiliation":[{"name":"Department of Computer Science & Engineering, Kakatiya University, Warangal 506009, Telangana, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7099-2269","authenticated-orcid":false,"given":"N.","family":"Ramana","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, Kakatiya University, Warangal 506009, Telangana, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"S0218126625503815BIB001","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging6120131"},{"key":"S0218126625503815BIB002","doi-asserted-by":"publisher","DOI":"10.1007\/s13735-020-00195-x"},{"key":"S0218126625503815BIB004","doi-asserted-by":"publisher","DOI":"10.3390\/s21020369"},{"key":"S0218126625503815BIB005","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-7166-0_16"},{"key":"S0218126625503815BIB006","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.11.013"},{"key":"S0218126625503815BIB007","first-page":"643","volume":"5","author":"Sethy P. 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