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Artificial Intelligence-Based Breast and Cervical Cancer Diagnosis and Management System

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Artificial Intelligence and Digitalization for Sustainable Development (ICAST 2022)

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Abstract

Breast cancer and cervical cancer are two of the most common and deadly malignancies in women. Early diagnosis and treatment can save lives and improve quality of life. However, there is a shortage of pathologists and physicians in most develo** countries, including Ethiopia, preventing many breast and cervical cancer patients from early cancer screening. Many women, particularly in low resource settings, have limited access to early diagnosis of breast and cervical cancer and receive poor treatment which in turn increases the morbidity and mortality due to these cancers. In this paper, an integrated intelligent decision support system is proposed for the diagnosis and management of breast and cervical cancer using multimodal im-age data. The system includes breast cancer type, sub-type and grade classification, cervix type (transformation zone) detection and classification, pap smear image classification, and histopathology-based cervical cancer type classification. In addition, patient registration, data retrieval, and storage as well as cancer statistical analysis mechanisms are integrated into the proposed system. A ResNet152 deep learning model was used for classification tasks and satisfactory results were achieved when testing the model. The developed system was deployed to an offline web page which has added the advantage of storing the digital medical images and the labeled results for future use by the physicians or other researchers.

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Authors and Affiliations

  1. Biomedical Imaging Unit, School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia

    Elbetel Taye Zewde & Gizeaddis Lamesgin Simegn

  2. AI and Biomedical Imaging Research Unit, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia

    Elbetel Taye Zewde, Mizanu Zelalem Degu & Gizeaddis Lamesgin Simegn

  3. Faculty of Computing and Informatics, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia

    Mizanu Zelalem Degu

Authors
  1. Elbetel Taye Zewde
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  2. Mizanu Zelalem Degu
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  3. Gizeaddis Lamesgin Simegn
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Corresponding author

Correspondence to Gizeaddis Lamesgin Simegn .

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Editors and Affiliations

  1. Bahir Dar University, Bahir Dar, Ethiopia

    Bereket H. Woldegiorgis

  2. Western University, London, ON, Canada

    Kibret Mequanint

  3. Bahir Dar Institute of Technology, Bahir Dar, Ethiopia

    Mekuanint A. Bitew

  4. Bahir Dar University, Bahir Dar, Ethiopia

    Teketay B. Beza

  5. Bahir Dar University, Bahir Dar, Ethiopia

    Abdulkerim M. Yibre

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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zewde, E.T., Degu, M.Z., Simegn, G.L. (2023). Artificial Intelligence-Based Breast and Cervical Cancer Diagnosis and Management System. In: Woldegiorgis, B.H., Mequanint, K., Bitew, M.A., Beza, T.B., Yibre, A.M. (eds) Artificial Intelligence and Digitalization for Sustainable Development. ICAST 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 455. Springer, Cham. https://doi.org/10.1007/978-3-031-28725-1_6

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  • DOI: https://doi.org/10.1007/978-3-031-28725-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28724-4

  • Online ISBN: 978-3-031-28725-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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