Prediction and Classification of Aerosol Deposition in Lung Using CT Scan Images

Author:

Karthika K.,Lakshmi G. R. Jothi

Publisher

Springer International Publishing

Reference13 articles.

1. Hasan, A. M., Jalab, H. A., Meziane, F., Kahtan, H., & Al-Ahmad, A. S. (2019). Combining deep and handcrafted image features for MRI brain scan classification. IEEE, 7.

2. Anthimopoulos, M., Christodoulidis, S., Ebner, L., Geiser, T., Christe, A., & Mougiakakou, S. (2018). Semantic segmentation of pathological lung tissue with dilated fully convolutional networks. IEEE, Journal of Biomedical and Health Informatics.

3. Pang, T., Guo, S., Zhang, X., & Zhao, L. (2019). Automatic lung segmentation based on texture and deep features of HRCT images with interstitial lung disease. Hindawi, BioMed Research International, 2019, 1.

4. Bui, V. K. H., Moon, J.-y., Chae, M., Park, D., & Lee, Y.-C. Prediction of aerosol deposition in the human respiratory tract via computational models. Multidisciplinary Digital PublishingInstitute.

5. Veerakumar, & Ravichandran, C. G. (2013). Intensity, shape and size based detection of lung nodules from CT images. Springer.

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