ABSTRACT
Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans commonly caused by bacteria called Streptococcus pneumonia. The present project aimed to examine the risk factors for death due to pneumonia in young children. One or more in three deaths in Asia is caused due to pneumonia as reported by World Health Organization (WHO). Chest X-Rays which are used to diagnose pneumonia need expert radiotherapists for evaluation. Thus, developing an automatic system for detecting pneumonia would be beneficial and it can save lots of peoples life and help stopping and curing and control for treating the disease without any delay particularly in remote areas. Due to the success of deep learning algorithms in analyzing medical images, Convolutional Neural Networks (CNNs) have gained much attention for disease classification. In addition, features learned by pre-trained cnn models on large-scale datasets are much useful in image classification tasks. In this work, we appraise the functionality of cnn models utilized as feature-extractors followed by different classifiers for the classification of abnormal and normal chest x-rays. This project is developed in matlab.
PROJECT OUTPUT
PROJECT DEMO VIDEO
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