Abstract
Lung infections are serious medical conditions that affect the life of common man. Timely diagnosis of lung diseases like tuberculosis, pneumonia, lung cancer, chronic obstructive pulmonary disease etc. is very essential. Chest X-ray is one among the most frequently used diagnostic tool in detecting different lung diseases as it is very common and cost effective. Disease Classification from the Chest X-rays is a demanding task for radiologists and pulmonologists. Computer-Aided Diagnosis (CAD) systems assist doctors to make quantitative analysis from Chest X-rays in identifying underlying diseases. However the performance of these systems in making conclusions on the disease type from an X-ray image could further be improved for achieving best diagnostic accuracy. The non-availability of enough number of skilled radiologists make the situation more worse. To educate technicians and care providers for the surge in need within a shorter period is really impractical. To resolve the problem, researchers utilize technological advancements. Machine learning based algorithms may well be suited for early diagnosis of various lung problems, from the available chest X-ray image and could produce coherent results. Thus Chest X-ray images along with strong algorithms have the potential to quantify the severity of lung disease. This paper presents the detailed description of Publicly accessible Chest X-ray image datasets available for diagnosis of various lung abnormalities.
Published Version
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