An Efficient Fuzzy Logic-Integrated Hybrid Deep Learning Framework for Medical Diagnosis
adaptive neuro-fuzzy inference system (ANFIS); decision support systems; deep learning; fuzzy logic; intelligent healthcare systems; medical diagnosis
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.
Saranya, R.; Rajagopal, Manikandan; Ramprasath, J.; ThamaraiSelvi, K.; Leelavathy, S.
Fuzzy Information and Engineering;Volume;18;Issue;1;pp.1-18
https://doi.org/10.26599/FIE.2025.9270072 https://www.scopus.com/pages/publications/105038223936?origin=resultslist
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&DS, Chennai, 600123, India