A survey of image classification methods and techniques for improving classification performance
International Journal of Remote Sensing
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Lung cancer is a disease of uncontrolled cell growth in tissues of the lung, Lung cancer is one of the most common and deadly diseases in the world. Detection of lung cancer in its early stage is the key of its cure. In general, a measure for early stage lung cancer diagnosis mainly includes those utilizing X-ray chest films, CT, MRI, etc. Medical images mining is a promising area of computational intelligence applied to automatically analyzing patient's records aiming at the discovery of new knowledge potentially useful for medical decision making. Firstly we will use some processes are essential to the task of medical image mining, Data Preprocessing, Feature Extraction and Rule Generation. The methods used in this paper work states, to classify the digital X-ray chest films into two categories: normal and abnormal. The normal state is the one that characterize a healthy patient. The abnormal state including the types of lung cancer; will be used as a common classification method indicating a machine learning method known as neural networks. In addition, we will investigate the use of association rules in the problem of x-ray chest films categorization. The digital x-ray chest films are storied in huge multimedia databases for a medical purpose. This multimedia database provides a great environment to apply some image recognition methods to extract the useful knowledge and then rules from the mentioned database. These rules that we could got using image recognition methods, will help the doctors to decide important decisions on a particular patient state.