Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Novel Deep Learning Approach for Identifying Interstitial Lung Diseases from HRCT Images Subject The topic of the resource Binary classification; Deep learning; HRCT; ILD; Multi-label classification Description An account of the resource Interstitial lung diseases (ILDs) are defined as a group of lung diseases that affect the interstitium and cause death among humans worldwide. It is more serious in underdeveloped countries as it is hard to diagnose due to the absence of specialists. Detecting and classifying ILD is a challenging task and many research activities are still ongoing. High-resolution computed tomography (HRCT) images have essentially been utilized in the diagnosis of this disease. Examining HRCT images is a difficult task, even for an experienced doctor. Information Technology, especially Artificial Intelligence, has started contributing to the accurate diagnosis of ILD from HRCT images. Similar patterns of different categories of ILD confuse doctors in making quick decisions. Recent studies have shown that corona patients with ILD also go on to sudden death. Therefore, the diagnosis of ILD is more critical today. Different deep learning approaches have positively impacted various image classification problems recently. The main objective of this proposed research work was to develop a deep learning model to classify the ILD categories from HRCT images. This proposed work aims to perform binary and multi-label classification of ILD using HRCT images on a customized VGG architecture. The proposed model achieved a high test accuracy of 95.18% on untrained data. 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Raju N.; Augustine D.P.; Anita H.B. Source A related resource from which the described resource is derived SN Computer Science, Vol-4, No. 2 Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s42979-022-01579-y" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42979-022-01579-y</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145388976&amp;doi=10.1007%2Fs42979-022-01579-y&amp;partnerID=40&amp;md5=f86e32cc9e314ad800bc4ebf2c7f778b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145388976&amp;doi=10.1007%2fs42979-022-01579-y&amp;partnerID=40&amp;md5=f86e32cc9e314ad800bc4ebf2c7f778b</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2662995X Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Raju N., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Augustine D.P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Anita H.B., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India