A ensemble methodology for automatic classification of chest X-rays using deep learning

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022-06

Autores

Luis Vogado (Autor) Outra; Flávio Araújo (Autor) Outra; Pedro Santos Neto (Autor) Outra; João Almeida (Autor) Outra; João Manuel R. S. Tavares (Autor) FEUP; Rodrigo Veras (Autor) Outra

Revista

Título: Computers in Biology and Medicine

Vol. 145 Nº 105442

Páginas: 1-15

ISSN: 0010-4825

Editora: Elsevier

Indexação

ISI Web of Knowledge - 0 Citações

ISI Web of Science

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Classificação Científica

CORDIS: Ciências Tecnológicas

FOS: Ciências médicas e da saúde

Outras Informações

ID Authenticus: P-00W-R6S

DOI: 10.1016/j.compbiomed.2022.105442

Abstract (EN): Chest radiographies, or chest X-rays, are the most standard imaging exams used in daily hospitals. Responsible for assisting in detecting numerous pathologies and findings that directly interfere in the patient's life, this exam is therefore crucial in screening patients. This work proposes a methodology based on a Convolutional Neural Networks (CNNs) ensemble to aid the diagnosis of chest X-ray exams by screening them with a high probability of being normal or abnormal. In the development of this study, a private dataset with frontal and lateral projections X-ray images was used. To build the ensemble model, VGG-16, ResNet50 and DenseNet121 architectures, which are commonly used in the classification of Chest X-rays, were evaluated. A Confidence Threshold (CTR) was used to define the predictions into High Confidence Normal (HCn), Borderline classification (BC), or High Confidence Abnormal (HCa). In the tests performed, very promising results were achieved: 54.63% of the exams were classified with high confidence; of the normal exams, 32% were classified as HCn with an false discovery rate (FDR) of 1.68%; and as to the abnormal exams, 23% were classified as HCa with 4.91% false omission rate (FOR).

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15