Autors: Gancheva, V. S., Georgiev I G., Todorova V.
Title: X-Ray Images Analytics Algorithm based on Machine Learning
Keywords: Covid-19, data analytics, image classification, machine lear

Abstract: The rapid development of information technology has led to a huge amount of data generated by large or complex systems and devices. Applications in information technology, medicine, and many other fields generate large volumes of data that challenge analysts. Data mining analysis finds application in areas where statistical and analytical methods and the models built through them are not sufficient. The paper discusses sources of medical data, use cases, and data analysis in medicine, as well as methods and algorithms for data analysis. The purpose and objectives of the study, presented in the paper are to propose an algorithm for processing X-Ray images based on tools and techniques from the field of machine learning. The preprocessing phase is concerned with image transformation, feature extraction, and the selection of training and testing datasets.

References

    Issue

    WSEAS Transactions on Information Science and Applications, vol. 20, pp. 136-145, 2023, Greece, DOI: 10.37394/23209.2023.20.16

    Full text of the publication

    Цитирания (Citation/s):
    1. Seck, Djamal and Diakité, Fatima. Supervised Machine Learning Models for the Prediction of Renal Failure in Senegal, Proceedings - 2023 International Conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2023, Pages 94 - 98, DOI 10.1109/ICCAIRO58903.2023.00022. - 2023 - в издания, индексирани в Scopus или Web of Science
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    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus