Computer Vision in Automatic Visceral Leishmaniasis Diagnosis: a Survey

Authors

  • Clésio Gonçalves Informatics Department, Federal Institute of Sertão Pernambucano, and Electrical Engineering - PPGEE/UFPI, Picos, Piauí, Brazil https://orcid.org/0000-0002-4773-8032
  • Armando Borges Information Systems - CSHNB/Universidade Federal do Piauí, Picos, Piauí, Brazil https://orcid.org/0000-0002-9955-7401
  • Anderson Rodrigues Information Systems - CSHNB Universidade Federal do Piauí, Picos, Piauí, Brazil https://orcid.org/0000-0001-7739-005X
  • Nathália Andrade Center for Intelligence on Emerging and Neglected Tropical Diseases (CIENTD), Teresina, Piauí, Brazil https://orcid.org/0000-0002-9338-6613
  • Marcos Lemus Computer Science Department Universidade Estadual do Piauí, Teresina, Piauí, Brazil https://orcid.org/0000-0002-8721-8226
  • Bruno Aguiar Department of Community Medicine , Universidade Federal do Piauí and Center for Intelligence on Emerging and Neglected Tropical Diseases (CIENTD), Teresina, Piauí, Brazil https://orcid.org/0000-0001-7986-1759
  • Romuere Silva Information Systems - CSHNB/UFPI, and Electrical Engineering - PPGEE/UFPI, and Center for Intelligence on Emerging and Neglected Tropical Diseases (CIENTD), Picos, Piauí, Brazil https://orcid.org/0000-0002-7163-7469

Keywords:

Visceral Leishmaniasis, Computer Vision, Automatic Detection

Abstract

Visceral Leishmaniasis (VL) is a neglected disease that affects 1 billion people in tropical and subtropical countries. In Brazil, VL causes about 3,500 cases/year. Although this disease is lethal when left untreated, the number of cases is increasing. Thus, it is necessary to study current and safety technologies for VL diagnosis, treatment, and control. Specialized laboratories carry out the LV diagnosis, and this step has great automation power through automatic methods based on computer vision to aid in diagnosis. This work aims to present state-of-the-art research on computer vision techniques to detect VL in humans and provide a theoretical basis for developing computational systems to aid in diagnosing VL. This work's contributions are finding the methodologies and algorithms used in VL automatic detection and listing the gaps in developing those systems. As a result, we find out the lack of image databases and the use of deep learning techniques is still scarce. We conclude that methodologies that use the segmentation procedure perform better in terms of accuracy and that it is possible to develop a CAD system to help diagnose VL in humans.

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Author Biographies

Clésio Gonçalves , Informatics Department, Federal Institute of Sertão Pernambucano, and Electrical Engineering - PPGEE/UFPI, Picos, Piauí, Brazil

Mestrando em Engenharia Elétrica na Universidade Federal do Piauí (UFPI) e professor de informática do Instituto Federal do Sertão Pernambucano - Campus Ouricuri. Possui especialização em Redes de Computadores (ESAB) e Banco de Dados (ISEPRO) e possui graduação em Análise e Desenvolvimento de Sistemas pelo Instituto Federal do Piauí (IFPI). Suas pesquisas se concentram em visão computacional aplicado a imagens médicas.

Armando Borges, Information Systems - CSHNB/Universidade Federal do Piauí, Picos, Piauí, Brazil

Estudante do curso de Bacharelado em Sistemas de Informação pela Universidade Federal do Piauí (UFPI). Suas pesquisas se concentram em visão computacional aplicado a imagens médicas.

Anderson Rodrigues, Information Systems - CSHNB Universidade Federal do Piauí, Picos, Piauí, Brazil

Estudante do curso de Bacharelado em Sistemas de Informação pela Universidade Federal do Piauí (UFPI). Suas pesquisas se concentram em visão computacional aplicado a imagens médicas.

Nathália Andrade, Center for Intelligence on Emerging and Neglected Tropical Diseases (CIENTD), Teresina, Piauí, Brazil

Possui graduação em Medicina Veterinária pela Universidade Federal do Piauí (2015). Tem experiência na área de Zoonoses, com ênfase em LV. Atualmente realiza projetos de pesquisa no Laboratório de Leishmaniose do Centro de Inteligência e Agravos Tropicais, Emergentes e Negligenciados (CIATEN/IDS), como "Criopreservação e clonagem de cepas de Leishmania" e "Classificação Automática de Leishmaniose".

Marcos Lemus, Computer Science Department Universidade Estadual do Piauí, Teresina, Piauí, Brazil

Auditor Fiscal de Controle Externo do Tribunal de Contas do Estado do Piauí, professor da Universidade Estadual do Piauí (UESPI) e pesquisador do Laboratório Aprendizado de Máquina e Big Data Analytics (Lambda). Mestre e Doutor em Informática Aplicada pela Universidade de Fortaleza (Unifor). Suas pesquisas se concentram em internet das coisas e inteligência computacional.

Bruno Aguiar, Department of Community Medicine , Universidade Federal do Piauí and Center for Intelligence on Emerging and Neglected Tropical Diseases (CIENTD), Teresina, Piauí, Brazil

Professor da Universidade Federal do Piauí (UFPI) e coordenador científico do núcleo de pesquisas Centro de Inteligência em Agravos Tropicais Emergentes e Negligenciados (CIATEN). Possui graduação em Biomedicina, mestrado em Ciências e Saúde (UFPI) e doutorado em microbiologia-imunologia (Université Laval, Canadá). Atua nas áreas de Saúde Pública, Microbiologia, Parasitologia e Biologia Molecular.

Romuere Silva, Information Systems - CSHNB/UFPI, and Electrical Engineering - PPGEE/UFPI, and Center for Intelligence on Emerging and Neglected Tropical Diseases (CIENTD), Picos, Piauí, Brazil

Professor da Universidade Federal do Piauí (UFPI) Campus Senador Helvídio Nunes de Barros, Picos. Doutor em Engenharia de Teleinformática pela Universidade Federal do Ceará, graduado e mestre em Ciência da Computação pela UFPI. Suas pesquisas se concentram em visão computacional aplicado a imagens médicas.

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Published

2022-10-11

How to Cite

Gonçalves , C. ., Borges, A. ., Rodrigues, A. ., Andrade, N., Lemus, M., Aguiar, B., & Silva, R. (2022). Computer Vision in Automatic Visceral Leishmaniasis Diagnosis: a Survey. IEEE Latin America Transactions, 21(2), 310–319. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/6920

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