TY - GEN
T1 - Automatic Diagnosis of Breast Cancer from Histopathological Images Using Deep Learning Technique
AU - Zewde, Elbetel Taye
AU - Simegn, Gizeaddis Lamesgin
N1 - Publisher Copyright:
© 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2022
Y1 - 2022
N2 - Breast cancer is the primary cause of women cancer death globally. Advancement in screening methods and early diagnosis can increase survival from breast cancer. Clinical breast examination, imaging, and pathological assessment are common techniques of a breast cancer screening. Biopsy test is the standard breast cancer screening method due to its ability to identify types and sub-types of cancer. However, current diagnosis using this method is generally made by visual inspection. The manual technique is time taking, dreary, and subjective, that can also lead to misdiagnosis. The current article proposes an automatic diagnosis system for breast cancer based on the deep learning neural network model. The model was trained and validated on histopathological images obtained from online data sets and local data obtained from Jimma University Medical Center using a digital camera mounted on a microscope. All images were pre-processed and enhanced before being fed into the previously trained ResNet 50 model. The developed technique is able to classify breast cancer into benign and malignant and to their subtypes. The results of our test showed that the proposed technique is 96.75%, 96.7% and 95.78% for the benign subtype and the malignant subtype classification, respectively. The developed technique has a potential to be used as a computer aided diagnosis system for clinicians, particularly in low resources setting, where both resources and experience are limited.
AB - Breast cancer is the primary cause of women cancer death globally. Advancement in screening methods and early diagnosis can increase survival from breast cancer. Clinical breast examination, imaging, and pathological assessment are common techniques of a breast cancer screening. Biopsy test is the standard breast cancer screening method due to its ability to identify types and sub-types of cancer. However, current diagnosis using this method is generally made by visual inspection. The manual technique is time taking, dreary, and subjective, that can also lead to misdiagnosis. The current article proposes an automatic diagnosis system for breast cancer based on the deep learning neural network model. The model was trained and validated on histopathological images obtained from online data sets and local data obtained from Jimma University Medical Center using a digital camera mounted on a microscope. All images were pre-processed and enhanced before being fed into the previously trained ResNet 50 model. The developed technique is able to classify breast cancer into benign and malignant and to their subtypes. The results of our test showed that the proposed technique is 96.75%, 96.7% and 95.78% for the benign subtype and the malignant subtype classification, respectively. The developed technique has a potential to be used as a computer aided diagnosis system for clinicians, particularly in low resources setting, where both resources and experience are limited.
KW - Breast cancer
KW - Cancer sub-type
KW - Classification
KW - Grading
KW - ResNet
KW - Transfer learning
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U2 - 10.1007/978-3-030-93709-6_42
DO - 10.1007/978-3-030-93709-6_42
M3 - Conference contribution
AN - SCOPUS:85122577803
SN - 9783030937089
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 619
EP - 634
BT - Advances of Science and Technology - 9th EAI International Conference, ICAST 2021, Proceedings
A2 - Berihun, Mulatu Liyew
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th EAI International Conference on Advancement of Science and Technology, ICAST 2021
Y2 - 27 August 2021 through 29 August 2021
ER -