Autors: Ivanova, D. A.
Title: Artificial Intelligence in Internet of Medical Imaging Things: The Power of Thyroid Cancer Detection
Keywords: Artificial Intelligence, Internet of Medical Imaging Things,

Abstract: The paper proposed an approach for thyroid cancer detection based on artificial intelligence in Internet of Medical Imaging Things (IoMIT) ecosystem. Ultrasonic imaging collected in IoMIT ecosystem is the best way for thyroid cancer diagnosis. Image segmentation and detection of benign and malignant thyroid nodules is an important part of the proposed approach. It is implemented in Apache Spark using MLlib based on Convolutional Neural Networks (CNNs). Finally, the results of medical imaging analytics are discussed.

References

    Issue

    International Conference on Information Technologies (InfoTech), vol. 18227624, 2018, Bulgaria, IEEE, DOI 10.1109/InfoTech.2018.8510725

    Copyright IEEE

    Цитирания (Citation/s):
    1. Thyroid image classification algorithm using DT CWT - 2019 - в издания, индексирани в Scopus или Web of Science
    2. Real Time Monitoring of Medical Images and Nursing Intervention After Heart Valve Replacement - 2020 - в издания, индексирани в Scopus или Web of Science
    3. Internet of Things Medical Image Detection and Pediatric Renal Failure Dialysis Complicated with Respiratory Tract Infection - 2021 - в издания, индексирани в Scopus или Web of Science
    4. Al Hakim, R. R., Satria, M. H., Arief, Y. Z., Setiawan, A. D., Pangestu, A., & Hidayah, H. A. (2021). Artificial Intelligence for Thyroid Disorders: A Systematic Review. Science in Information Technology Letters, 2(2). - 2021 - в издания, индексирани в Scopus или Web of Science
    5. Habchi, Y., Himeur, Y., Kheddar, H., (...), Ouamane, A., Mansoor, W., AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions, Systems 11(10), 519 - 2023 - в издания, индексирани в Scopus или Web of Science
    6. Patel, J., Patel, J., Kapdi, R., Patel, S., Early prediction of prevalent diseases using IoMT ( Book Chapter), Federated Learning for Internet of Medical Things: Concepts, Paradigms, and Solutions pp. 125-144 - 2023 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Web of Science