Pneumonia Detection and Classification Using Deep Learning on Chest X-Ray Images

Abstract

Pneumonia is a bacterial infection caused people of all ages with mild to severe inflammation of the lung tissue. The best known and most common clinical method for the diagnosis of pneumonia is chest X-ray imaging. But the diagnosis of pneumonia from chest Xray images is a difficult task, even for specialist radiologists. In developing countries, this lung disease becomes one of the deadliest among children under the age of 5 and causing 15% of deaths recorded annually. Therefore, in this study, firstly the presence of the disease was tried to be determined using chest X-ray dataset. In addition, using the bacterial and viral pneumonia classes which are two different types of pneumonia, multi class classification which consists of viral pneumonia, bacterial pneumonia and healthy has been done. Since the used dataset does not have a balanced distribution among all classes, SMOTE (Synthetic Minority Over-sampling Technique) method has been used to deal with imbalanced dataset. CNN model and models in Ensemble Learning have been created from scratch instead of using weights of pre-trained networks to see the effectiveness of CNN weights on medical data. For each classification problem, two different deep learning methods which are CNN and ensemble learning have been used and 95% average accuracy has been obtained for each model, for binary classification and 78% and 75% average accuracy has been obtained for each model respectively for multi class classification problem.

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images

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A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder

“…Each of 3 layers in the encoder part consists of a 2D-Convolution and an activation function. Convolution layers have been used as a feature extractor [16], and as an activation function, Leaky-ReLU has been used. The choice of activation function for a model is an important step to good accurate results [17].…”

Detection of Pneumonia From X-Ray Images Using Deep Learning Techniques

“…As a result of the research, a success rate of 93.01% has been achieved in the first stage of the model, while a very high success rate of 97.22% has been obtained in the second stage. In [14], two different deep learning and machine learning-based models have been proposed for conducting multi-class and binary classification over the used the used X-Ray image dataset. Also, SMOTE algorithm, which is one of the oversampling methods used to make the image distribution in the classes equal and to overcome the imbalance data problems, has been used for balancing the dataset.…”