International Journal of Advances in Applied Computational Intelligence
Journal DOI
2833-5600ISSN (Online)
Volume 5 , Issue 1 , PP: 29-39, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed Hatip 1 * , Necati Olgun 2 , Sandy Montajab Hazzouri 3
Doi: https://doi.org/10.54216/IJAACI.050103
Machine Learning (ML) and Artificial Intelligence (AI) are being employed in the fight against COVID19 by supporting the analysis of medical images, like X-rays and CT scans, to find characteristic paradigms linked with the virus. AI methods can evaluate huge volumes of data, which includes imaging data and patient medical records, for enriching the speed and precision of COVID19 diagnosis. Also, the use of ML and AI in medical imaging can aid in detecting new variants of viruses and forecasting their spread. The integration of ML and AI in COVID19 healthcare has greater potential to enhance the efficiency and accuracy of diagnoses along with that informing public health decision-making. Thus, the study proposes a Farmland Fertility Optimization Algorithm with Deep Learning based Healthcare Decision Making (FFOADL-HDM) approach for the detection of COVID19. The presented FFOADL-HDM approach emphasises the identification and classification of COVID19 using a CT scan. To achieve this, the FFOADL-HDM method exploits a modified SqueezeNet model for the generation of feature vector. Also, the hyperparameters of the modified SqueezeNet model can be selected by the use of FFOA. At last, the COVID-19 detection procedure is executed by the use of Adamax optimizer with (CFNN). The stimulation analysis of the FFOADL-HDM algorithm is studied on the SARS-CoV-2 CT image dataset from the Kaggle repository. The results highlighted the improved detection rate of the FFOADL-HDM technique over recent state of art approaches
Artificial Intelligence , COVID19 diagnoses , Computed tomography scans , Machine learning , Healthcare , Decision making
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