{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:27:42Z","timestamp":1787027262608,"version":"build-2736575974"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T00:00:00Z","timestamp":1655683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:p>Detection of malignant lung nodules from Computed Tomography (CT) images is a significant task for radiologists. But, it is time-consuming in nature. Despite numerous breakthroughs in studies on the application of deep learning models for the identification of lung cancer, researchers and doctors still face challenges when trying to deploy the model in clinical settings to achieve improved accuracy and sensitivity on huge datasets. In most situations, deep convolutional neural networks are used for detecting the region of the main nodule of the lung exclusive of considering the neighboring tissues of the nodule. Although the accuracy achieved through CNN is good enough but this models performance degrades when there are variations in image characteristics like: rotation, tiling, and other abnormal image orientations. CNN does not store relative spatial relationships among features in scanned images. As CT scans have high spatial resolution and are sensitive to misalignments during the scanning process, there is a requirement of a technique which helps in considering spatial information of image features also. In this paper, a hybrid model named VCNet is proposed by combining the features of VGG-16 and capsule network (CapsNet). VGG-16 model is used for object recognition and classification. CapsNet is used to address the shortcomings of convolutional neural networks for image rotation, tiling, and other abnormal image orientations. The performance of VCNeT is verified on the Lung Image Database Consortium (LIDC) image collection dataset. It achieves higher testing accuracy of 99.49% which is significantly better than MobileNet, Xception, and VGG-16 that has achieved an accuracy of 98, 97.97, and 96.95%, respectively. Therefore, the proposed hybrid VCNet framework can be used for the clinical purpose for nodule detection in lung carcinoma detection.<\/jats:p>","DOI":"10.3389\/fpubh.2022.894920","type":"journal-article","created":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T01:57:48Z","timestamp":1655690268000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["VCNet: Hybrid Deep Learning Model for Detection and Classification of Lung Carcinoma Using Chest Radiographs"],"prefix":"10.3389","volume":"10","author":[{"given":"Ritu","family":"Tandon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shweta","family":"Agrawal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arthur","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahab S.","family":"Band","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,6,20]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1056\/NEJMoa1102873","article-title":"Reduced lung-cancer mortality with low-dose computed tomographic screening","volume":"365","year":"2011","journal-title":"N Engl J Med"},{"key":"B2","doi-asserted-by":"publisher","first-page":"4376","DOI":"10.31838\/jcr.07.11.592","article-title":"Rice plant diseases detection and classification using deep learning models: a systematic review","volume":"7","author":"Agrawal","year":"2020","journal-title":"J Crit Rev."},{"key":"B3","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/978-3-030-40850-3_11","article-title":"Medical text and image processing: applications, issues and challenges","volume-title":"Machine Learning with Health Care Perspective","author":"Agrawal","year":"2020"},{"key":"B4","doi-asserted-by":"publisher","first-page":"10","DOI":"10.31838\/jcr.07.15.779","article-title":"Sequential Convolutional Neural Network for Automatic Breast cancer image classification using Histopathological Images","volume":"7","author":"Tandon","year":"2020","journal-title":"Methods."},{"key":"B5","doi-asserted-by":"publisher","first-page":"357","DOI":"10.3389\/fpubh.2020.00357","article-title":"COVID-19 patient health prediction using boosted random forest algorithm","volume":"8","author":"Iwendi","year":"2020","journal-title":"Front Public Health"},{"key":"B6","doi-asserted-by":"publisher","first-page":"90777","DOI":"10.1109\/ACCESS.2019.2924584","article-title":"Socio-Technological factors affecting user's adoption of eHealth functionalities: a case study of China and Ukraine eHealth systems","volume":"7","author":"Kutia","year":"2019","journal-title":"IEEE Access"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11831-019-09344-w","article-title":"A survey of deep learning and its applications: a new paradigm to machine learning","volume":"27","author":"Dargan","year":"2020","journal-title":"Arch Comput Methods Eng"},{"key":"B8","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1504\/IJESMS.2021.115534","article-title":"Agrawal S. 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