{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T18:07:40Z","timestamp":1769710060336,"version":"3.49.0"},"reference-count":21,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,11,4]]},"abstract":"<jats:p>Lung cancer is the prevalent malignancy afflicting both men and women, mostly affects the chain smokers. The lung CT images are examined to identifying the abnormalities, but diagnosing lung cancer with CT images is time-consuming and difficult task. In this work, a novel Sooty-LuCaNet has been proposed in which the best features are selected using sooty tern optimization to reduces computational complexity of neural network. Initially, the denoised CT images are segmented using Grabcut technique to separate the lung nodules by eliminating the background distortions. The deep learning based Shufflenet is used to extract the structural features from the segmented nodule and the textural features from the enhanced images. Afterwards, the sooty tern optimization (STO) algorithm is applied to select the most relevant features from the extracted features from the ShuffleNet. Finally, the classification process is carried out to differentiate the normal, small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) from the CT images. The experimental findings show the robustness of the proposed Sooty-LuCaNet based on the specific metrics namely sensitivity, accuracy, specificity, recall, precision and F1 score. An average classification accuracy of 99.16% is achieved for detection and classification of lung cancer.<\/jats:p>","DOI":"10.3233\/jifs-232875","type":"journal-article","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T11:16:03Z","timestamp":1691493363000},"page":"8823-8836","source":"Crossref","is-referenced-by-count":1,"title":["Sooty-LuCaNet: Sooty tern optimization based deep learning network for lung cancer detection"],"prefix":"10.1177","volume":"45","author":[{"given":"B.","family":"Muthazhagan","sequence":"first","affiliation":[{"name":"Department of Computer Science Engineering, Kings Engineering College, Irungattukottai, Chennai, Tamil Nadu, India"}]},{"given":"T.","family":"Ravi","sequence":"additional","affiliation":[{"name":"Department of Computer Science Engineering, Shadan Womens College of Engineering and Technology, Khairatabad, Hyderabad, Telangana, India"}]},{"given":"D.","family":"Rajinigirinath","sequence":"additional","affiliation":[{"name":"Department of Computer Science Engineering, Sri Muthukumaran Institute of Technology, Chennai, Tamil Nadu, India"}]}],"member":"179","reference":[{"issue":"3","key":"10.3233\/JIFS-232875_ref2","first-page":"335","article-title":"Lung cancer: Epidemiology and screening","volume":"102","author":"Oliver","year":"2022","journal-title":"Surgical Clinics"},{"key":"10.3233\/JIFS-232875_ref4","doi-asserted-by":"crossref","first-page":"103791","DOI":"10.1016\/j.bspc.2022.103791","article-title":"Lung cancer diagnosis in CT images based on Alexnet optimized by modified Bowerbird optimization algorithm","volume":"77","author":"Xu","year":"2022","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"10.3233\/JIFS-232875_ref5","first-page":"7","article-title":"Cancer statistics","volume":"72","author":"Siegel","year":"2022","journal-title":"CA: A Cancer Journal for Clinicians"},{"issue":"2","key":"10.3233\/JIFS-232875_ref6","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1080\/09553002.2021.1846818","article-title":"The risk of induced cancer and ischemic heart disease following low dose lung irradiation for COVID-19: Estimation based on a virtual case","volume":"97","author":"Arruda","year":"2021","journal-title":"Int J Radiat Biol"},{"issue":"3","key":"10.3233\/JIFS-232875_ref8","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1016\/j.ygyno.2019.03.011","article-title":"Placental site trophoblastic tumor and epithelioid trophoblastic tumor: Clinical and pathological features, prognostic variables and treatment strategy","volume":"153","author":"Gadducci","year":"2019","journal-title":"Gynecologic Oncology"},{"issue":"7802","key":"10.3233\/JIFS-232875_ref9","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1038\/s41586-020-2140-0","article-title":"Integrating genomic features for non-invasive early lung cancer detection","volume":"580","author":"Chabon","year":"2020","journal-title":"Nature"},{"key":"10.3233\/JIFS-232875_ref10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11704-020-9050-z","article-title":"DFD-Net: Lung cancer detection from denoised CT scan image using deep learning","volume":"15","author":"Sori","year":"2021","journal-title":"Front Comput Sci"},{"key":"10.3233\/JIFS-232875_ref11","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.patrec.2019.11.014","article-title":"Lungs cancer classification from CT images: An integrated design of contrast based classical features fusion and selection","volume":"129","author":"Khan","year":"2020","journal-title":"Pattern Recognit Lett"},{"issue":"1","key":"10.3233\/JIFS-232875_ref12","doi-asserted-by":"crossref","first-page":"28","DOI":"10.3390\/ai1010003","article-title":"Deep learning for lung cancer nodules detection and classification in CT scans","volume":"1","author":"Riquelme","year":"2020","journal-title":"Ai"},{"issue":"1","key":"10.3233\/JIFS-232875_ref14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-84630-x","article-title":"Deep learning classification of lung cancer histology using CT images","volume":"11","author":"Chaunzwa","year":"2021","journal-title":"Sci Rep"},{"key":"10.3233\/JIFS-232875_ref16","doi-asserted-by":"crossref","first-page":"7731","DOI":"10.1007\/s11042-019-08394-3","article-title":"Deep learning for lung Cancer detection and classification","volume":"79","author":"Asuntha","year":"2020","journal-title":"Multimedia Tools Appl"},{"issue":"04","key":"10.3233\/JIFS-232875_ref17","doi-asserted-by":"crossref","first-page":"175","DOI":"10.36548\/jiip.2020.4.002","article-title":"Early diagnosis of lung cancer with probability of malignancy calculation and automatic segmentation of lung CT scan images","volume":"2","author":"Manoharan","year":"2020","journal-title":"Journal of Innovative Image Processing (JIIP)"},{"key":"10.3233\/JIFS-232875_ref18","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.eswa.2019.05.041","article-title":"Automated 3-D lung tumor detection and classification by an active contour model and CNN classifier","volume":"134","author":"Kasinathan","year":"2019","journal-title":"Expert Syst Appl"},{"issue":"5","key":"10.3233\/JIFS-232875_ref19","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1038\/s42256-020-0173-6","article-title":"A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets","volume":"2","author":"Mukherjee","year":"2020","journal-title":"Nat Mach Intell"},{"issue":"4","key":"10.3233\/JIFS-232875_ref20","doi-asserted-by":"crossref","first-page":"339","DOI":"10.18280\/ts.360406","article-title":"Lung cancer detection basedon CT scan images by using deep transfer learning","volume":"36","author":"Sajja","year":"2019","journal-title":"Traitementdu Signal"},{"key":"10.3233\/JIFS-232875_ref21","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.patrec.2019.11.013","article-title":"Deep-learning framework to detect lung abnormality\u2013A study with chest X-Ray and lung CT scan images","volume":"129","author":"Bhandary","year":"2020","journal-title":"Pattern Recognit Lett"},{"issue":"5","key":"10.3233\/JIFS-232875_ref22","doi-asserted-by":"crossref","first-page":"940","DOI":"10.3390\/app9050940","article-title":"Mehr, Classification of pulmonary CT images by using hybrid 3D-deep convolutional neural network architecture","volume":"9","author":"Polat","year":"2019","journal-title":"Appl Sci"},{"key":"10.3233\/JIFS-232875_ref24","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1016\/j.measurement.2019.05.027","article-title":"Lung cancer detection from CT image using improved profuse clustering and deep learning instantaneously trained neural networks","volume":"145","author":"Shakeel","year":"2019","journal-title":"Meas"},{"issue":"1","key":"10.3233\/JIFS-232875_ref25","doi-asserted-by":"crossref","first-page":"9297","DOI":"10.1038\/s41598-020-66333-x","article-title":"Weakly-supervised learning for lung carcinoma classification using deep learning","volume":"10","author":"Kanavati","year":"2020","journal-title":"Sci Rep"},{"issue":"1","key":"10.3233\/JIFS-232875_ref26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1504\/IJBET.2016.074108","article-title":"Enhancement of fissure using back propagation neural network and segmentation of lobes in CT scan image","volume":"20","author":"Thanammal","year":"2016","journal-title":"Int J Biomed Eng Technol"},{"key":"10.3233\/JIFS-232875_ref28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3218574","article-title":"RAGCN: Region aggregation graph convolutional network for bone age assessment from X-ray images","volume":"71","author":"Li","year":"2022","journal-title":"IEEE Transactions on Instrumentation and Measurement"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-232875","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T07:18:07Z","timestamp":1769671087000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-232875"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,4]]},"references-count":21,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3233\/jifs-232875","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,4]]}}}