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Classifying Chest Pathology Images Using Deep Learning Techniques
Angielski
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Chest radiographs are the most common examination in radiology in today's era. They are essential and very helpful for the supervision of various diseases associated with high mortality and display a wide range of potential information about various diseases. The most common verdicts in chest X-rays include Tuberculosis, Cardiomegaly & Mediastinum chest diseases. Distinguishing the various chest pathologies is a difficult task even to the human observer and for radiologist. Therefore, there is an interest in developing computer system diagnosis to assist radiologists in reading chest images through machine. The healthy versus pathology detection i.e. Tuberculosis and Cardiomegaly in chest radiography was explored using Laplacian of Gaussian (LoG), Local Binary Patterns (LBP), Speed up Robust Features (SURF) and also used the Bag-of-Visual-Words (BoVW) model using Artificial Neural Network (ANN) & Deep Learning techniques that classifies between healthy vs pathological cases.
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