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However, most of the earlier ILD classification work involves time\u2010consuming manual identification of the region of interest (ROI) from the lung HRCT image before applying the deep learning classification algorithm. This paper has developed a two\u2010stage hybrid approach of deep learning networks for ILD classification. A conditional generative adversarial network (c\u2010GAN) has segmented the lung part from the HRCT images at the first stage. The c\u2010GAN with multiscale feature extraction module has been used for accurate lung segmentation from the HRCT images with lung abnormalities. At the second stage, a pretrained ResNet50 has been used to extract the features from the segmented lung image for classification into six ILD classes using the support vector machine classifier. The proposed two\u2010stage algorithm takes a whole HRCT as input eliminating the need for extracting the ROI and classifies the given HRCT image into an ILD class. The performance of the proposed two\u2010stage deep learning network\u2010based ILD classifier has improved considerably due to the stage\u2010wise improvement of deep learning algorithm performance.<\/jats:p>","DOI":"10.1155\/2022\/7340902","type":"journal-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T23:50:14Z","timestamp":1643759414000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Two\u2010Stage Hybrid Approach of Deep Learning Networks for Interstitial Lung Disease Classification"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0661-8285","authenticated-orcid":false,"given":"Swati P.","family":"Pawar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanjay N.","family":"Talbar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,2]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.4143\/crt.2017.119"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1002\/9781118919408"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2012.2190984"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2012.2198829"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1118\/1.1624771"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2017.2731873"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-019-01976-1"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-018-1819-y"},{"key":"e_1_2_8_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"e_1_2_8_10_2","unstructured":"SimonyanK.andZissermanA. 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