Deep learning for X-ray image classification

• Autor(a) principal: Narciso, Teresa Costeira
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10773/38579
• topic: Atelectasis; Cardiomegaly; Deep-learning; Effusion; Nodule; ResNet34; X-ray images

This work aims to develop a deep learning model to correctly classify common chest pathologies like atelectasis, cardiomegaly, effusion and nodule as well as cases where none of these pathologies are present (labelled "no-findings") in frontal-view chest X-ray images. Two different models were created using the ResNet34 architecture: one with the classic single-label multi-class approach and the other with joined binary classifiers for atelectasis vs no-findings, cardiomegaly vs no-findings, effusion vs no-findings and nodule vs no-findings. The first classifier achieved an Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve of 0.73 for atelectasis, 0.91 for cardiomegaly, 0.82 for effusion, 0.67 for the "no-findings" and 0.74 for nodule. The second classifier achieved an AUC of 0.73 for atelectasis, 0.90 for cardiomegaly, 0.78 for effusion, 0.67 for "no-findings" and 0.74 for nodule. The multi-class multi-label approach revealed better results compared with the joined binary classifiers. Both models achieved state of the art results in the cardiomegaly and effusion classes. The nodule and the "no-findings" classes presented the worst mislabeling results. This called for further analysis of the models revealing the difficulty the model had on the "no-findings" class i.e, the model mislabeled the "no-findings" images with high confidence. After more closely inspecting the images in this class, the poor quality of in the images was noticed, revealing instances of possible mislabeled images and images where the lungs were out of focus.