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.

Convolutional neural network for assisting accuracy of personalized clavicle bone implant designs

“…The convolutional neural network method that used in this study is successful in providing the CNN test results of 97.4% for the classification of clavicle bone images. Compared with other research that has carried out the CNN algorithm, such as to classify pneumonia detection on chest X-rays with a final accuracy percentage of 88.14% [34] and research on a new algorithm called improved dial's loading algorithm (IDLA) using a CNN model that combines digital CT image processing and machine learning to identify cancer cells through IDLA automatically with minimum iterations with an accuracy percentage of 92.81% [35], this research improves the results.…”