Chronic Disease Diagnosis Using Multi-modal Transfer Learning Model
Chronic disease diagnosis; Limited data; Multi-modal data; Transfer learning
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.
Guha, Debanksh; Avtaran, Divya Kumar; Sharma, Vandana; Mishra, Sushruta; Lenka, Rahul; Alkhayyat, Ahmed
Lecture Notes in Networks and Systems;Volume;1409 LNNS;pp.57-67
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-981-96-6300-2_5 https://www.scopus.com/pages/publications/105015770662?origin=resultslist
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