DEEP LEARNING FOR CLASSIFICATION AND LOCALIZATION OF PNEUMOIA MARKERS IN POINT-OF-CARE LUNG ULTRA SOUND

DEEP LEARNING FOR CLASSIFICATION AND LOCALIZATION OF PNEUMOIA MARKERS IN POINT-OF-CARE LUNG ULTRA SOUND

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Abstract - PNEUMONIA is a rapidly spreading viral disease that infects not only humans, but animals are also infected because of this disease. The daily life of human beings, their health, and the economy of a country are affected due to this deadly viral disease. PNEUMONIA is a common spreading disease, and till now, not a single country can prepare a vaccine for PNEUMONIA. A clinical study of PNEUMONIA infected patients has shown that these types of patients are mostly infected from a lung infection after coming in contact with this disease. Chest x-ray (i.e., radiography) and chest Computed tomography (CT) are a more effective imaging technique for diagnosing lunge related problems. Still, a substantial chest x-ray is a lower cost process in comparison to chest CT. Deep learning is the most successful technique of machine learning, which provides useful analysis to study a large amount of chest x-ray images that can critically impact on screening of PNEUMONIA. This type have taken the PA (posteroanterior) view of chest x-ray scans for PNEUMONIA affected patients as well as healthy patients. After cleaning up the images and applying data augmentation, we have used deep learning-based SVM models for classification and accuracy.