Model development for pneumonia detection from chest radiograph using transfer learning

Abstract

Accurate interpretation of chest radiographs outcome in epidemiological studies facilitates the process of correctly identifying chest-related or respiratory diseases. Despite the fact that radiological results have been used in the past and is being continuously used for diagnosis of pneumonia and other respiratory diseases, there abounds much variability in the interpretation of chest radiographs. This variability often leads to wrong diagnosis due to the fact that chest diseases often have common symptoms. Moreover, there is no single reliable test that can identify the symptoms of pneumonia. Therefore, this paper presents a standardized approach using convolutional neural network (CNN) and transfer learning technique for identifying pneumonia from chest radiographs that ensure accurate diagnosis and assist physicians in making precise prescriptions for the treatment of pneumonia. A training set consisting of 5,232 optical coherence tomography and chest X-ray images dataset from Mendelev public database was used for this research and the performance evaluation of the model developed on the test set yielded 88.14% accuracy, 90% precision, 85% recall and F1 score of 0.87.