Multi-label Classification of Pathologies in Chest Radiograph Images Using DenseNet

Resumo


Chest radiography exams are still one of the main methods for detecting and diagnosing certain thoracic pathologies. This study evaluates the performance of a DenseNet in a multi-label classification task on radiography images, using focal loss as the loss function to address the class imbalance problem. For the experiments, 14 different types of findings were considered. Satisfactory results were obtained using the area under the ROC curve (AUC-ROC) as the metric, where the average performance across all classes was 0.861.
Publicado
25/09/2023
MENDES, Alison Corrêa; PESSOA, Alexandre César Pinto; PAIVA, Anselmo Cardoso de. Multi-label Classification of Pathologies in Chest Radiograph Images Using DenseNet. In: BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), 12. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 167-180. ISSN 2643-6264.