{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T14:37:00Z","timestamp":1775227020625,"version":"3.50.1"},"reference-count":29,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T00:00:00Z","timestamp":1636934400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJICC"],"published-print":{"date-parts":[[2022,7,6]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Computed tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. To detect the location of the cancerous lung nodules, this work uses novel deep learning methods. The majority of the early investigations used CT, magnetic resonance and mammography imaging. Using appropriate procedures, the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer. All of the methods used to discover and detect cancer illnesses are time-consuming, expensive and stressful for the patients. To address all of these issues, appropriate deep learning approaches for analyzing these medical images, which included CT scan images, were utilized.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>Radiologists currently employ chest CT scans to detect lung cancer at an early stage. In certain situations, radiologists' perception plays a critical role in identifying lung melanoma which is incorrectly detected. Deep learning is a new, capable and influential approach for predicting medical images. In this paper, the authors employed deep transfer learning algorithms for intelligent classification of lung nodules. Convolutional neural networks (VGG16, VGG19, MobileNet and DenseNet169) are used to constrain the input and output layers of a chest CT scan image dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer, squamous and adenocarcinoma impacted chest CT scan images. According to the confusion matrix results, the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy, followed by VGG19 with 89.39%, MobileNet with 85.60% and DenseNet169 with 83.71% accuracy, which is analyzed using Google Collaborator.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19, MobileNet and DenseNet169. The results are validated by computing the confusion matrix for each network type.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijicc-07-2021-0147","type":"journal-article","created":{"date-parts":[[2021,11,14]],"date-time":"2021-11-14T22:12:57Z","timestamp":1636927977000},"page":"345-362","source":"Crossref","is-referenced-by-count":16,"title":["Intelligent classification of lung malignancies using deep learning techniques"],"prefix":"10.1108","volume":"15","author":[{"given":"Priyanka","family":"Yadlapalli","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5290-4146","authenticated-orcid":false,"given":"D.","family":"Bhavana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suryanarayana","family":"Gunnam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2021,11,15]]},"reference":[{"issue":"5","key":"key2022070515514094300_ref001","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1158\/1078-0432.CCR-17-2289","article-title":"The impact of smoking and TP53 mutations in lung adenocarcinoma patients with targetable mutations\u2014the lung cancer mutation consortium (LCMC2)","volume":"24","year":"2018","journal-title":"Clinical Cancer Research"},{"issue":"8","key":"key2022070515514094300_ref002","first-page":"409","article-title":"Lung cancer detection and classification with 3D convolutional neural network (3D-CNN)","volume":"8","year":"2017","journal-title":"Lung Cancer"},{"key":"key2022070515514094300_ref003","first-page":"1","article-title":"Image classification method in DR image based on transfer learning","year":"2018"},{"issue":"4","key":"key2022070515514094300_ref004","first-page":"6524","article-title":"Lung nodule detection from CT scans using Gaussian mixture convolutional AutoEncoder and convolutional neural network","volume":"25","year":"2021","journal-title":"Annals of the Romanian Society for Cell Biology"},{"issue":"6","key":"key2022070515514094300_ref005","first-page":"1","article-title":"DON: deep learning and optimization-based framework for detection of novel Coronavirus disease using x-ray images","volume":"13","year":"2021","journal-title":"Interdisciplinary Sciences: Computational Life Sciences"},{"key":"key2022070515514094300_ref006","doi-asserted-by":"crossref","unstructured":"Fayemiwo, M.A., Olowookere, T.A., Arekete, S.A., Ogunde, A.O., Odim, M.O., Oguntunde, B.O., Olaniyan, O.O., Ojewumi, T.O. and Oyetade, I.S. 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