Reconhecimento de padrões em imagens radiográficas de tórax: apoiando o diagnóstico de doenças pulmonares infecciosas
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2023-09-29
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Universidade Federal de Goiás
Resumo
Pattern Recognition (PR) is a field of computer science that aims to develop techniques and
algorithms capable of identifying regularities in complex data, enabling intelligent systems to
perform complicated tasks with precision. In the context of diseases, PR plays a crucial role in
diagnosis and detection, revealing patterns hidden from human eyes, assisting doctors in making
decisions and identifying correlations. Infectious pulmonary diseases (IPD), such as pneumonia,
tuberculosis, and COVID-19, challenge global public health, causing thousands of deaths annually,
affecting healthcare systems, and demanding substantial financial resources. Diagnosing them can
be challenging due to the vagueness of symptoms, similarities with other conditions, and
subjectivity in clinical assessment. For instance, chest X-ray (CXR) examinations are a tedious and
specialized process with significant variation among observers, leading to failures and delays in
diagnosis and treatment, especially in underdeveloped countries with a scarcity of radiologists. In
this thesis, we investigate PR and Artificial Intelligence (AI) techniques to support the diagnosis of
IPID in CXRs. We follow the guidelines of the World Health Organization (WHO) to support the goals
of the 2030 Agenda, which includes combating infectious diseases. The research questions involve
selecting the best techniques, acquiring data, and creating intelligent models. As objectives, we
propose low-cost, high-efficiency, and effective PR and AI methods that range from preprocessing
to supporting the diagnosis of IPD in CXRs. The results so far align with the state of the art, and we
believe they can contribute to the development of computer-assisted IPD diagnostic systems.
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FONSECA, A. U. Reconhecimento de padrões em imagens radiográficas de tórax: apoiando o diagnóstico de doenças pulmonares infecciosas. 2023. 183 f. Tese (Doutorado em Ciência da Computação) - Instituto de Informática, Universidade Federal de Goiás, Goiânia, 2023.