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 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/. Creator An entity primarily responsible for making the resource Saranya, R.; Rajagopal, Manikandan; Ramprasath, J.; ThamaraiSelvi, K.; Leelavathy, S. Title A name given to the resource An Efficient Fuzzy Logic-Integrated Hybrid Deep Learning Framework for Medical Diagnosis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Fuzzy Information and Engineering;Volume;18;Issue;1;pp.1-18 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.26599/FIE.2025.9270072" target="_blank" rel="noreferrer noopener">https://doi.org/10.26599/FIE.2025.9270072</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038223936?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038223936?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 Saranya R., Dr. SNS Rajalakshmi College of Arts and Science, Department of Computer Science with Cyber Security, Coimbatore, 600049, India; Rajagopal M., School of Business and Management, Christ University, Bangalore, 600871, India; Ramprasath J., Dr. Mahalingam College of Engineering and Technology, Department of Information Technology, Pollachi, 642003, India; ThamaraiSelvi K., New Horizon College of Engineering, Department of Information Science and Engineering, Bangalore, 600671, India; Leelavathy S., Panimalar Engineering College, Department of AI&amp;DS, Chennai, 600123, India Description An account of the resource Medical diagnosis involves analyzing symptoms, test results, and patient histories, but uncertainty from vague symptoms and incomplete records complicates the process. Fuzzy logic-based systems address this issue but often depend on manual rule creation, which is time-consuming. This research proposes a hybrid approach integrating fuzzy logic with deep learning techniques (FL-DLT) for intelligent diagnosis. The framework combines adaptive neuro-fuzzy inference system (ANFIS) for handling uncertainty with convolutional neural networks (CNNs) for extracting features from medical images like X-rays and MRIs. ANFIS models relationships between symptoms, results, and diagnoses, while CNNs analyze medical images. Experimental results show high accuracy and reliability, even with noisy or incomplete data. The proposed approach can improve diagnostic accuracy and efficiency, supporting clinicians in decision-making. Key contributions include the development of the FL-DLT framework and its evaluation using a large dataset of patient records and medical images. Additionally, the research offers insights into the application of fuzzy logic and deep learning in medical diagnosis, highlighting their potential to enhance diagnostic outcomes and efficiency in clinical practice. 2009 Tsinghua University Press. Subject The topic of the resource adaptive neuro-fuzzy inference system (ANFIS); decision support systems; deep learning; fuzzy logic; intelligent healthcare systems; medical diagnosis Publisher An entity responsible for making the resource available Tsinghua University Press Relation A related resource ISSN: 16168658; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online