CONSTRUCTION OF A CONVOLUTIONAL NEURAL NETWORK MODEL FOR DETECTION OF PULMONARY DISEASES IN X-RAY IMAGES
Abstract
In 2019, lower respiratory infections affected approximately 489 million people worldwide, with pneumonia accounting for around 4 million annual deaths, representing 7% of all fatalities. This disease is one of the leading causes of global mortality, with the greatest impact in developing countries, particularly among children under five and elderly individuals over seventy years old. The diagnosis of pulmonary diseases frequently relies on chest X-rays, which reveal severity through pulmonary opacities and pleural fluid accumulation. This study developed a model based on Convolutional Neural Networks (CNN) to automatically detect pulmonary pathologies in X-ray images. The model achieved an accuracy above 80%, with the confusion matrix indicating 91.8% precision for normal cases and 69.5% for pathological cases. The solution was integrated into a web application for the classification of radiographic images.
Author Biographies
Professor de doutorado, pesquisador, escritor e engenheiro de inteligência artificial. Doutorado em Recursos Naturais com ênfase em modelos de otimização baseados em programação linear (UFCG, Brasil). Mestrado em Ciência da Computação (UFCG, Brasil). Diplomado em Ciência da Computação (UFCG, Brasil) e Radiologia (Faculdade Mauricio de Nassau, Brasil). Professor e Pesquisador da Universidade Estadual da Paraíba (UEPB), Campina Grande, PB, Brasil.
Universidade Estadual da Paraíba.
Mestrando em Biometria e Estatística Aplicada - UFRPE - Universidade Federal Rural de Pernambuco.
Professora no Departamento de Estatística - UEPB. Pesquisadora vinculada ao CIDACS - BA. Universidade Federal Rural de Pernambuco.
Universidade Estadual da Paraíba.
Professor Titular do DEINFO - UFRPE - Universidade Federal Rural de Pernambuco.
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