Deep Learning for Pneumonia Classification in Chest Radiography Images using Wavelet Transform

Abstract

Chronic respiratory diseases constitute a prognostic severity factor for some respiratory illnesses. A case in point is pneumonia, a lung infection, whose effective management requires highly accurate diagnosis and precise treatment. Categorizing pneumonia as positive or negative does go through a process of classifying chest radiography images. This task plays a crucial role in medical diagnostics as it facilitates the detection of pneumonia and helps in making timely treatment decisions. Deep learning has shown remarkable effectiveness in various medical imaging applications, including the recognition and categorization of pneumonia in chest radiography images. The main aim of this research is to compare the efficacy of two convolutional neural network models for classifying pneumonia in chest radiography images. The first model was directly trained on the original images, achieving a training accuracy of 0.9266, whereas the second model was trained on images transformed using wavelets and achieved a training accuracy of 0.94. The second model demonstrated significantly superior results in terms of accuracy, sensitivity, and specificity.