Interstitial Lung Disease (ILD) are a group of diseases are due to inflammation of lung tissues. Due to unknown cause of
the ILDs international multidisciplinary consensus conference, American Thoracic Society and European Respiratory society
proposed classification for ILDs. ILD diagnosis involves various stages of questioning and physical examination, testing, x-ray and
CT scan. As such, the purpose of this study was to list out the methodologies for classification of ILD disease from medical images
and discuss about their metiers and softness. In depth literature survey reveals that there are many methods for classifying ILD
disease but very few methodologies uses machine learning issues. In this paper we are discussing about the various lung patterns
using different methods like Local Binary Pattern in the process of using the convolutional neural networks. Such that the
convolutional neural networks are used in the paper for comparing the various results from the various data sets that are used
from the university hospital of Geneva and from Bern University Hospital which consists of HRCT scans and also used the datasets
from the publicly available databases of ILD cases used in the “Near-Affine-Invariant Texture learning for lung tissue analysis
using isotropic wavelet frames”.
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