{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T15:13:56Z","timestamp":1770045236699,"version":"3.49.0"},"reference-count":27,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2021,3,22]],"date-time":"2021-03-22T00:00:00Z","timestamp":1616371200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2021,11,17]]},"abstract":"<jats:p>Lung cancer is the most common cancer throughout the world and identification of malignant tumors at an early stage is needed for diagnosis and treatment of patient thus avoiding the progression to a later stage. In recent times, deep learning architectures such as CNN have shown promising results in effectively identifying malignant tumors in CT scans. In this paper, we combine the CNN features with texture features such as Haralick and Gray level run length matrix features to gather benefits of high level and spatial features extracted from the lung nodules to improve the accuracy of classification. These features are further classified using SVM classifier instead of softmax classifier in order to reduce the overfitting problem. Our model was validated on LUNA dataset and achieved an accuracy of 93.53%, sensitivity of 86.62%, the specificity of 96.55%, and positive predictive value of 94.02%.<\/jats:p>","DOI":"10.3233\/jifs-189847","type":"journal-article","created":{"date-parts":[[2021,3,23]],"date-time":"2021-03-23T13:26:20Z","timestamp":1616505980000},"page":"5243-5251","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Lung nodule classification using combination of CNN, second and higher order texture features"],"prefix":"10.1177","volume":"41","author":[{"given":"Amrita","family":"Naik","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, National Institute of Technology, Ponda, Goa, India"}]},{"given":"Damodar Reddy","family":"Edla","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, National Institute of Technology, Ponda, Goa, India"}]}],"member":"179","published-online":{"date-parts":[[2021,3,22]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21492"},{"key":"e_1_3_1_3_2","doi-asserted-by":"crossref","unstructured":"ByrneS.C. BarrettB. and BhatiaR. The Impact of Diagnostic Imaging Wait Times on the Prognosis of Lung Cancer Canadian Association of Radiologists Journal 66(1) (2015) 53\u201357.","DOI":"10.1016\/j.carj.2014.01.003"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.06.015"},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","unstructured":"JinBH. LiZ. TongR. and LinL. A deep 3D residual CNN for false-positive reduction in pulmonarynoduledetection \u201dMedical Physics 45(5) 2097\u20132107 2018.","DOI":"10.1002\/mp.12846"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-017-1696-0"},{"key":"e_1_3_1_7_2","doi-asserted-by":"crossref","unstructured":"NibaliA. HeZ. and WollersheimD. Pulmonary nodule classification with deep residual networks International Journal of Computer Assisted Radiology and Surgery pp. 1\u201310 2017.","DOI":"10.1007\/s11548-017-1605-6"},{"key":"e_1_3_1_8_2","unstructured":"AlexK. SutskeverI. and HintonG.E. Imagenet classification with deep convolutional neural networks In Advances in neural information processing systems pp. 1097\u20131105 2012."},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2004.07.008"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1973.4309314"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0146-664X(75)80008-6"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/0167-8655(90)90112-F"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/0167-8655(91)80014-2"},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","unstructured":"CortesC. and VapnikV.N. Support-vector networks Machine Learning 20(3) (1995) 273\u2013297.","DOI":"10.1007\/BF00994018"},{"key":"e_1_3_1_15_2","unstructured":"KumarA. MukhopadhyayS. KhandelwalN. 3d texture analysis of solitary pulmonary nodules using co-occurrence matrix from volumetric lung CT images Proceedings of the SPIE Medical Imaging Conference: 28 February 2013; Lake Buena Vista Florida USA. Bellingham WA: SPIE; 2013. p. 1\u20134."},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.7314\/APJCP.2013.14.10.6019"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.06.015"},{"key":"e_1_3_1_18_2","doi-asserted-by":"crossref","unstructured":"HeK. ZhangX. RenS. and SunJ. Deep Residual Learning for Image Recognition in CVPR 2016.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_1_19_2","doi-asserted-by":"crossref","unstructured":"WangZ. XuH. and SunM. Deep Learning Based Nodule Detection from Pulmonary CT Images 2017 10th International Symposium on Computational Intelligence and Design (ISCID).","DOI":"10.1109\/ISCID.2017.107"},{"key":"e_1_3_1_20_2","doi-asserted-by":"crossref","unstructured":"ChenJ. and ShenY. The Effect of Kernel Size of CNNs for Lung Nodule Classification 2017 9th International Conference on Advanced Infocomm Technology.","DOI":"10.1109\/ICAIT.2017.8388942"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2016.12.007"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.08.022"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.05.029"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2018.10.011"},{"key":"e_1_3_1_25_2","doi-asserted-by":"crossref","unstructured":"LyuJ. and LingS.H. Using Multi-level Convolutional Neural Network for Classification of Lung Nodules on CT images 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2018).","DOI":"10.1109\/EMBC.2018.8512376"},{"key":"e_1_3_1_26_2","doi-asserted-by":"crossref","unstructured":"SoriW.J. FengJ. and LiuS. Multi-path convolutional neural network for lung cancer detection Multidim Syst Sign Process (2018). https:\/\/doi.org\/10.1007\/s11045-018-0626-9","DOI":"10.1007\/s11045-018-0626-9"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2903587"},{"key":"e_1_3_1_28_2","doi-asserted-by":"crossref","unstructured":"HusseinS. GilliesR. CaoK. SongQ. UlasBagci TUMORNET: Lung nodule characterization using multi-view convolutional neural network with gaussian process IEEE International Symposium on Biomedical Imaging (ISBI) 2017.","DOI":"10.1109\/ISBI.2017.7950686"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-189847","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-189847","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-189847","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T03:19:57Z","timestamp":1770002397000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-189847"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,22]]},"references-count":27,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,11,17]]}},"alternative-id":["10.3233\/JIFS-189847"],"URL":"https:\/\/doi.org\/10.3233\/jifs-189847","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,22]]}}}