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Transfer Learning for Highlighting Diagnosis in Pathological Anatomy Based on Immunohistochemistry

Transfer Learning for Highlighting Diagnosis in Pathological Anatomy Based on Immunohistochemistry

Mohamed Gasmi, Issam Bendib, Yasmina Benmabrouk
Copyright: © 2021 |Volume: 16 |Issue: 4 |Pages: 23
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781799859819|DOI: 10.4018/IJHISI.301232
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MLA

Gasmi, Mohamed, et al. "Transfer Learning for Highlighting Diagnosis in Pathological Anatomy Based on Immunohistochemistry." IJHISI vol.16, no.4 2021: pp.1-23. http://doi.org/10.4018/IJHISI.301232

APA

Gasmi, M., Bendib, I., & Benmabrouk, Y. (2021). Transfer Learning for Highlighting Diagnosis in Pathological Anatomy Based on Immunohistochemistry. International Journal of Healthcare Information Systems and Informatics (IJHISI), 16(4), 1-23. http://doi.org/10.4018/IJHISI.301232

Chicago

Gasmi, Mohamed, Issam Bendib, and Yasmina Benmabrouk. "Transfer Learning for Highlighting Diagnosis in Pathological Anatomy Based on Immunohistochemistry," International Journal of Healthcare Information Systems and Informatics (IJHISI) 16, no.4: 1-23. http://doi.org/10.4018/IJHISI.301232

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Abstract

In the medical field, the diagnostic phase is the most important, as the entire treatment process will be based on this step. Oncological diseases such as breast cancer require a precise anatomopathological study accompanied most of the time by an immunohistochemical study whose goal is to know the sensitivity of tumor tissues to hormone therapy and targeted therapy. This study relies on antibodies and their interpretation requires significant time and as it can suffer from poor reproducibility which negatively influences the treatment stage. In this work, the objective is to classify histopathological images stained with E-cadherin antibody to help pathologists in their work in order to facilitate oncologists in the choice of the most appropriate therapeutic protocol. The realization of this task is based on the choice of transfer learning as techniques and data augmentation due to the minimal number of images gathered. The results obtained are very satisfying both on accuracy where we reached a rate of 97.27% with a reduced number of parameters and very close to our basic model.