Pneumonia is a life-threatening lung infection resulting from several different viral infections. Early detection of pneumonia is crucial for determining the appropriate treatment of the disease and preventing and possible preventive measures. Chest radiographs are the most widely used tool for diagnosing pneumonia; however, they are subject to inter-class variability, and similarity to other pulmonary diseases, and the diagnosis largely depends on the clinicians’ expertise in detecting early pneumonia traces.
To assist medical practitioners, a Convolutional Neural Networks CNN-based deep learning model was developed to detect and classify Chest X-ray images into two classes “Pneumonia” and “Normal. Class Activation Mapping (CAM) was also applied to the detected images to intensify the potential regions of pneumonia in CXR images.
The dataset used contains 5,856 Chest X-Ray images (in JPEG format), 3 folders named train, test and val, and 2 categories Pneumonia and Normal. The dataset can be found here.
Below are the details of the 3 folders in the dataset.
After analyzing over 5,000 image-dataset, a model was developed with Convolutional Neural Networks CNN. The developed model gave achieved an accuracy of 83.81%. This can be used in a medical imaging hospital to diagnose potential patients who may be suffering from pneumonia, as early detection could aid better treatment.