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 Faculty Publications Conference Paper Faculty Publications- Conference Papers 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/. Creator An entity primarily responsible for making the resource Guha, Debanksh; Avtaran, Divya Kumar; Sharma, Vandana; Mishra, Sushruta; Lenka, Rahul; Alkhayyat, Ahmed Title A name given to the resource Chronic Disease Diagnosis Using Multi-modal Transfer Learning Model Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1409 LNNS;pp.57-67 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-6300-2_5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-6300-2_5</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015770662?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015770662?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Guha D., Kalinga Institute of Industrial Technology, Bhubaneswar, India; Avtaran D.K., Kalinga Institute of Industrial Technology, Bhubaneswar, India; Sharma V., Computer Science Department, CHRIST University, Bengaluru, India; Mishra S., Kalinga Institute of Industrial Technology, Bhubaneswar, India; Lenka R., College of Technical Engineering, Islamic University, Najaf, Iraq; Alkhayyat A., College of Technical Engineering, Islamic University, Najaf, Iraq Description An account of the resource Chronic diseases are on rise in recent times and symptoms of these disorders need to be detected at an early stage. Traditional methods in handling these risks are quite resource intensive and costly with less accuracy. This paper explores transfer learning to address the challenge of limited data in diagnosing chronic diseases like chronic kidney disease, breast cancer, hepatitis, and Alzheimers. We propose a multi-modal framework utilizing pre-trained models: image recognition for medical images and natural language processing for textual data. Transfer learning aims to improve diagnostic accuracy and reduce training time, enabling development of adaptable tools for various chronic diseases. The implementation results show the performance of the model is promising generating an accuracy rate of 94%. Also the model gave a mean accuracy of 93.3% when tested with different chronic disorders. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Chronic disease diagnosis; Limited data; Multi-modal data; Transfer learning Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981966299-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online