An AI Based Multi-Modal System for Thyroid Disease Using Xgboost and Resnet18

Authors

  • Dr. V. Priya Professor, Department of Computer Scinece Engineering.Paavai Engineering College, Namakkal, TamilNadu. Author
  • M. Kaviya Shree Dep Scholar, Department of Computer Science Engineering, Paavai Enginnering College, Namakkal, TamilNadu. Author
  • P. Nandhini Priya Dep Scholar, Department of Computer Science Engineering, Paavai Enginnering College, Namakkal, TamilNadu. Author
  • K. Pavithra Dep Scholar, Department of Computer Science Engineering, Paavai Enginnering College, Namakkal, TamilNadu. Author

DOI:

https://doi.org/10.47392/IRJAEH.2026.0429

Keywords:

Attendance system, Computer vision, Face detection, Facial recognition, Image processing

Abstract

Thyroid disease are common disorders requiring timely diagnosis. This project presents a multi-modal AI system combining XGBoost for structured clinical data and ResNet18 for thyroid imaging to classify and predict thyroid disease accurately. The system integrates predictions using a fusion module and provides medicine suggestions, automated medical reports, and an AI chatbot to guide patients on whether to visit a hospital. Experimental results show improved accuracy over single-model approaches, offering a reliable, real-time and interpretable solution to assist clinicians in early detection, treatment planning, and better patient outcomes.

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Published

2026-05-11

How to Cite

An AI Based Multi-Modal System for Thyroid Disease Using Xgboost and Resnet18. (2026). International Research Journal on Advanced Engineering Hub (IRJAEH), 4(05), 3340-3344. https://doi.org/10.47392/IRJAEH.2026.0429