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Aoki, T., Nakaata, H., Watanabe, H., Nakamura, K., Kasai, T., Hashimoto, H., Yasumoto, K., Kido, M. (2000). Evolution of peripheral lung adenocarcinomas: CT findings correlated with histology and tumor doubling times. American Journal of Roentgenology, 174 (3), 763-768. https://doi.org/10.2214/ajr.174.3.1740763Search in Google Scholar
Arun, R., Singaravelan, S. (2020). Automated communication system for detection of lung cancer using catastrophe features. Informatologia, 53 (3-4), 184-190. https://doi.org/10.32914/i.53.3-4.5Search in Google Scholar
Alam, J., Alam, S., Hossan, A. (2018). Multi-stage lung cancer detection and prediction using multi-class SVM classifie. In 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2). IEEE. http://dx.doi.org/10.1109/IC4ME2.2018.8465593Search in Google Scholar
Alizadeh, G., Frounchi, J., Baradaran Nia, M., Asgarifar, S., Zarifi, M. H. (2008). An FPGA implementation of an Artificial Neural Network for prediction of cetane number. In 2008 International Conference on Computer and Communication Engineering. IEEE, 605-608. https://doi.org/10.1109/ICCCE.2008.4580675Search in Google Scholar
Sathees Kumar, B., Sathiyaprasad, B. (2021). Bone cancer detection using feature extraction with classification using k-nearest neighbor and decision tree algorithm. In Smart Intelligent Computing and Communication Technology. IOS Press, APC Vol. 38, 347-353. http://dx.doi.org/10.3233/APC210064Search in Google Scholar
Satheeshkumar, B., Sathiyaprasad, B. (2022). Medical data analysis using feature extraction and classification based on machine learning and metaheuristic optimization algorithm. In Applications of Computational Science in Artificial Intelligence. IGI Global, 132-156. https://doi.org/10.4018/978-1-7998-9012-6.ch006Search in Google Scholar
Parameswari, A., Vinoth Kumar, K., Gopinath, S. (2022). Thermal analysis of Alzheimer’s disease prediction using random forest classification model. Materials Today: Proceedings, 66 (3), 815-821. https://doi.org/10.1016/j.matpr.2022.04.357Search in Google Scholar
Sathiyaprasad, B., Satheesh Kumar, B. (2022). Multi spectral image retrieval in remote sensing big data using fast recurrent convolutional neural network. In 2022 International Conference for Advancement in Technology (ICONAT). IEEE. https://doi.org/10.1109/ICONAT53423.2022.9725921Search in Google Scholar
El-Baz, A., Gimel'farb, G., Falk, R., El-Ghar, M. A. (2007). A new CAD system for early diagnosis of detected lung nodules. In 2007 IEEE International Conference on Image Processing. IEEE, 461-464. https://doi.org/10.1109/ICIP.2007.4379192Search in Google Scholar
Lin, D.-T., Yan, C.-R. (2002). Lung nodules identification rules extraction with neural fuzzy network. In Proceedings of the 9th International Conference on Neural Information Processing. IEEE, 2049-2053. https://doi.org/10.1109/ICONIP.2002.1199035Search in Google Scholar
Vinod, D. N., Prabaharan, S. R. S. (2023). COVID-19-The role of artificial intelligence, machine learning, and deep learning: A newfangled. Archives of Computational Methods in Engineering, 30 (4), 2667-2682. https://doi.org/10.1007%2Fs11831-023-09882-4Search in Google Scholar
Vinod, D. N., Prabaharan, S. R. S. (2023). Elucidation of infection asperity of CT scan images of COVID-19 positive cases: A Machine Learning perspective. Scientific African, 20, e01681. https://doi.org/10.1016%2Fj.sciaf.2023.e01681Search in Google Scholar
Storcz, T., Várady, G., Ercsey, Z. (2021). Identification of shadowed areas to improve ragweed leaf segmentation. Tehnical Gazette, 28 (4), 1236-1243. https://doi.org/10.17559/TV-20190604092100Search in Google Scholar
Winkler, A. M., Renaud, O., Smith, S. M., Nichols, T. E. (2020). Permutation inference for canonical correlation analysis. NeuroImage, 220, 117065. https://doi.org/10.1016/j.neuroimage.2020.117065Search in Google Scholar
Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H. R., Xu, D. (2022). UNETR: Transformers for 3D medical image segmentation. In 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 574-584. https://doi.org/10.1109/WACV51458.2022.00181Search in Google Scholar
Ivković, R., Petrović, M., Daković, B., Jakšić, B., Milošević, I. (2020). Segmentation and classification of Bi-Rads medical images with the imaging biomarkers according to level of detail. Tehnical Gazette, 27 (2), 527-534. https://doi.org/10.17559/TV-20181221151205Search in Google Scholar