Paper Title:A Comparitive Study of Medical Image Classification Using Machine Learning Methods


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”.