CNN Based Autoencoder Application in Breast Cancer Image Retrieval

Content Based Medical Image Retrieval (CBMIR) is considered as a common technique to retrieve relevant images by comparing the features contained in the query image with the features contained in the image located in the database. Currently, the study related to CBMIR on breast cancer image however...

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Veröffentlicht in:2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) S. 29 - 34
Hauptverfasser: Minarno, Agus Eko, Ghufron, Kharisma Muzaki, Sabrila, Trifebi Shina, Husniah, Lailatul, Sumadi, Fauzi Dwi Setiawan
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 21.07.2021
Schlagworte:
Autoencoder
Biomedical imaging
Breast cancer
CNN
Feature extraction
Image retrieval
Seminars
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Abstract Content Based Medical Image Retrieval (CBMIR) is considered as a common technique to retrieve relevant images by comparing the features contained in the query image with the features contained in the image located in the database. Currently, the study related to CBMIR on breast cancer image however remains challenging due to inadequate research in such area. Previous study has a low performance and misinformation emphasizing the feature extraction process. Therefore, this study aims to utilize the CNN based Autoencoder method to minimize misinformation in the feature extraction process and to improve the performance result. The dataset used in this study is the BreakHis dataset. Overall, the results of image retrieval in breast cancer applying the CNN based Autoencoder method achieved higher performance compared to the method used in the previous study with an average precision of 0.9237 in the mainclass dataset category and 0.6825 in the subclass dataset category.
AbstractList Content Based Medical Image Retrieval (CBMIR) is considered as a common technique to retrieve relevant images by comparing the features contained in the query image with the features contained in the image located in the database. Currently, the study related to CBMIR on breast cancer image however remains challenging due to inadequate research in such area. Previous study has a low performance and misinformation emphasizing the feature extraction process. Therefore, this study aims to utilize the CNN based Autoencoder method to minimize misinformation in the feature extraction process and to improve the performance result. The dataset used in this study is the BreakHis dataset. Overall, the results of image retrieval in breast cancer applying the CNN based Autoencoder method achieved higher performance compared to the method used in the previous study with an average precision of 0.9237 in the mainclass dataset category and 0.6825 in the subclass dataset category.
Author Ghufron, Kharisma Muzaki
Sabrila, Trifebi Shina
Husniah, Lailatul
Minarno, Agus Eko
Sumadi, Fauzi Dwi Setiawan
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  givenname: Trifebi Shina
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  email: fauzisumadi@umm.ac.id
  organization: Universitas Muhammadiyah Malang,Informatics Department,Malang,Indonesia
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Snippet Content Based Medical Image Retrieval (CBMIR) is considered as a common technique to retrieve relevant images by comparing the features contained in the query...
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StartPage 29
SubjectTerms Autoencoder
Biomedical imaging
Breast cancer
CNN
Feature extraction
Image retrieval
Seminars
Title CNN Based Autoencoder Application in Breast Cancer Image Retrieval
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