Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{155228,
        author = {R. Anitha and M. Saranya and C. Vijayashanthi},
        title = {COVID-19 Detected Automatically From CT Images With Ensemble Learning And A Convolutional Neural Network},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {1},
        pages = {313-318},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155228},
        abstract = {Corona Virus Disease (COVID-19), caused by a novel coronavirus, is a worldwide pandemic that has claimed millions of lives. The disease is still not under control a year after it first appeared. COVID-19 is resurfacing in several countries, posing a major threat. COVID-19 can be contained and diagnosed early, preventing large-scale spread. COVID-19 diagnosis with RT-PCR is the gold standard. . However, in most parts of the world, the ability to perform RT-PCR tests is limited. CT is useful in the detection of COVID-19 studies. As a readily available and less expensive modality, CT could be used as a substitute for RT-PCR tests in areas where RT-PCR testing is not available. Machine intelligence algorithms may extract information from CT images and classify them into COVID-19 and non-COVID categories. The most extensively used machine intelligence technology for disease prediction from photos is the convolutional neural network (CNN). COVID-19 identification using various pre-trained CNNS is compared in this work.},
        keywords = {COVID-19; Auto detections; CAT; Machine intelligence techniques},
        month = {},
        }