Deep Learning for Condition Detection in Chest Radiographs: A Performance Comparison of Different Radiograph Views and Handling of Uncertain Labels

Chest radiographs are the initial diagnostic modality for lung or chest-related conditions. It is believed that radi- ologist’s availability is a bottleneck impacting patient’s safety because of long waiting times. With the arrival of machine learning and especially deep learning, the race for finding artificial intelligence (AI) based approaches that allow for the highest accuracy in detecting abnormalities on chest radiographs is at its peak. classification of radiographs as normal or ab- normal is based on the training and expertise of the reporting radiologist. The increase in the number of chest radiographs over a period of time and the lack of sufficient radiologists in the UK and worldwide have had an impact on the number of chest radiographs assessed and reported in a given time frame. Substantial work is dedicated to machine learning for classifying normal and abnormal radiographs based on a single pathology. The success of deep learning techniques in binary radiograph classification urges the medical imaging community to apply it to multi-label radiographs. Deep learning techniques often require huge datasets to train its underlying model. Recently, the availability of large multi-label datasets has ignited new efforts to overcome this challenging task. This work presents multiple convolutional neural networks (CNNs) based models trained on publically available CheXpert multi-label data. Based on common pathologies seen on chest radiographs and their clinical significance, we have chosen pathologies such as pulmonary odema, cardiomegaly, atelectasis, consolidation and pleural ef- fusion. We trained our models using different projections such as anteroposterior (AP), posteroanterior (PA), and lateral and compared the performance of our models for each projection. Our results demonstrate that the model for the AP projection outperforms the remaining models with an average AUC of 0.85. Furthermore, we use the samples with uncertain labels in CheXpert dataset and improve the model performance by removing the uncertainty using gaussian mixture models (GMM). The results show improvement in all three views with AUCs ranging from 0.91 for AP, 0.75 for PA and 0.85 on the lateral view.Peer reviewe

Tuberculosis diagnosis from pulmonary chest x-ray using deep learning.

Doctoral Degree. University of KwaZulu-Natal, Durban.Tuberculosis (TB) remains a life-threatening disease, and it is one of the leading causes of mortality in developing countries. This is due to poverty and inadequate medical resources. While treatment for TB is possible, it requires an accurate diagnosis first. Several screening tools are available, and the most reliable is Chest X-Ray (CXR), but the radiological expertise for accurately interpreting the CXR images is often lacking. Over the years, CXR has been manually examined; this process results in delayed diagnosis, is time-consuming, expensive, and is prone to misdiagnosis, which could further spread the disease among individuals. Consequently, an algorithm could increase diagnosis efficiency, improve performance, reduce the cost of manual screening and ultimately result in early/timely diagnosis. Several algorithms have been implemented to diagnose TB automatically. However, these algorithms are characterized by low accuracy and sensitivity leading to misdiagnosis. In recent years, Convolutional Neural Networks (CNN), a class of Deep Learning, has demonstrated tremendous success in object detection and image classification task. Hence, this thesis proposed an efficient Computer-Aided Diagnosis (CAD) system with high accuracy and sensitivity for TB detection and classification. The proposed model is based firstly on novel end-to-end CNN architecture, then a pre-trained Deep CNN model that is fine-tuned and employed as a features extractor from CXR. Finally, Ensemble Learning was explored to develop an Ensemble model for TB classification. The Ensemble model achieved a new stateof- the-art diagnosis accuracy of 97.44% with a 99.18% sensitivity, 96.21% specificity and 0.96% AUC. These results are comparable with state-of-the-art techniques and outperform existing TB classification models.Author's Publications listed on page iii