Image Enhancement for Breast Cancer Detection on Screening Mammography Using Deep Learning

Muhammad Yusuf Kardawi; Riyanarto Sarno

doi:10.1109/ICCoSITE57641.2023.10127835

Image Enhancement for Breast Cancer Detection on Screening Mammography Using Deep Learning

Muhammad Yusuf Kardawi^*
, Riyanarto Sarno

^*Corresponding author for this work

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Mammography offers the most efficient approach for detecting breast illnesses early. Nevertheless, Image enhancement to improve breast cancer detection is required since mammograms are low-contrast and noisy images, and typical diagnostic markers such as microcalcifications and masses are challenging to identify. Due to this issue, this paper evaluates the impact of image enhancement on the performance of the deep learning approach and conducts qualitative and quantitative testing of various deep learning models in breast cancer classification. This study uses Mini Digital Database for Screening Mammography (Mini-DDSM) breast dataset containing cancer and normal images. The mammography images are then improved using morphological erosion and enhanced using two image enhancement algorithms which are Unsharp Masking (UM) and High-Frequency Emphasis Filtering (HEF). Deep learning classification algorithms such as ResNet, DenseNet, and EfficientNet are employed to classify breast cancer. Each architecture is compared and analyzed regarding the effect of the image enhancement techniques and achieves the highest 76.08% accuracy score on breast cancer classification in the mammography dataset using the HEF technique.

Original language	English
Title of host publication	ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering
Subtitle of host publication	Digital Transformation Strategy in Facing the VUCA and TUNA Era
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	585-590
Number of pages	6
ISBN (Electronic)	9798350320954
DOIs	https://doi.org/10.1109/ICCoSITE57641.2023.10127835
Publication status	Published - 2023
Event	2023 International Conference on Computer Science, Information Technology and Engineering, ICCoSITE 2023 - Jakarta, Indonesia Duration: 16 Feb 2023 → …

Publication series

Name	ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering: Digital Transformation Strategy in Facing the VUCA and TUNA Era

Conference

Conference	2023 International Conference on Computer Science, Information Technology and Engineering, ICCoSITE 2023
Country/Territory	Indonesia
City	Jakarta
Period	16/02/23 → …

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

High-Frequency Emphasis Filtering
Unsharp Masking
deep learning
image enhancement

Access to Document

10.1109/ICCoSITE57641.2023.10127835

Cite this

Kardawi, M. Y., & Sarno, R. (2023). Image Enhancement for Breast Cancer Detection on Screening Mammography Using Deep Learning. In ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering: Digital Transformation Strategy in Facing the VUCA and TUNA Era (pp. 585-590). (ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering: Digital Transformation Strategy in Facing the VUCA and TUNA Era). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCoSITE57641.2023.10127835

Kardawi, Muhammad Yusuf ; Sarno, Riyanarto. / Image Enhancement for Breast Cancer Detection on Screening Mammography Using Deep Learning. ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering: Digital Transformation Strategy in Facing the VUCA and TUNA Era. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 585-590 (ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering: Digital Transformation Strategy in Facing the VUCA and TUNA Era).

@inproceedings{f6281eabb8b842fdbaf1b4f98c25d098,

title = "Image Enhancement for Breast Cancer Detection on Screening Mammography Using Deep Learning",

abstract = "Mammography offers the most efficient approach for detecting breast illnesses early. Nevertheless, Image enhancement to improve breast cancer detection is required since mammograms are low-contrast and noisy images, and typical diagnostic markers such as microcalcifications and masses are challenging to identify. Due to this issue, this paper evaluates the impact of image enhancement on the performance of the deep learning approach and conducts qualitative and quantitative testing of various deep learning models in breast cancer classification. This study uses Mini Digital Database for Screening Mammography (Mini-DDSM) breast dataset containing cancer and normal images. The mammography images are then improved using morphological erosion and enhanced using two image enhancement algorithms which are Unsharp Masking (UM) and High-Frequency Emphasis Filtering (HEF). Deep learning classification algorithms such as ResNet, DenseNet, and EfficientNet are employed to classify breast cancer. Each architecture is compared and analyzed regarding the effect of the image enhancement techniques and achieves the highest 76.08\% accuracy score on breast cancer classification in the mammography dataset using the HEF technique.",

keywords = "High-Frequency Emphasis Filtering, Unsharp Masking, deep learning, image enhancement",

author = "Kardawi, \{Muhammad Yusuf\} and Riyanarto Sarno",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Computer Science, Information Technology and Engineering, ICCoSITE 2023 ; Conference date: 16-02-2023",

year = "2023",

doi = "10.1109/ICCoSITE57641.2023.10127835",

language = "English",

series = "ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering: Digital Transformation Strategy in Facing the VUCA and TUNA Era",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "585--590",

booktitle = "ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering",

address = "United States",

}

Kardawi, MY & Sarno, R 2023, Image Enhancement for Breast Cancer Detection on Screening Mammography Using Deep Learning. in ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering: Digital Transformation Strategy in Facing the VUCA and TUNA Era. ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering: Digital Transformation Strategy in Facing the VUCA and TUNA Era, Institute of Electrical and Electronics Engineers Inc., pp. 585-590, 2023 International Conference on Computer Science, Information Technology and Engineering, ICCoSITE 2023, Jakarta, Indonesia, 16/02/23. https://doi.org/10.1109/ICCoSITE57641.2023.10127835

Image Enhancement for Breast Cancer Detection on Screening Mammography Using Deep Learning. / Kardawi, Muhammad Yusuf; Sarno, Riyanarto.
ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering: Digital Transformation Strategy in Facing the VUCA and TUNA Era. Institute of Electrical and Electronics Engineers Inc., 2023. p. 585-590 (ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering: Digital Transformation Strategy in Facing the VUCA and TUNA Era).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Image Enhancement for Breast Cancer Detection on Screening Mammography Using Deep Learning

AU - Kardawi, Muhammad Yusuf

AU - Sarno, Riyanarto

PY - 2023

Y1 - 2023

N2 - Mammography offers the most efficient approach for detecting breast illnesses early. Nevertheless, Image enhancement to improve breast cancer detection is required since mammograms are low-contrast and noisy images, and typical diagnostic markers such as microcalcifications and masses are challenging to identify. Due to this issue, this paper evaluates the impact of image enhancement on the performance of the deep learning approach and conducts qualitative and quantitative testing of various deep learning models in breast cancer classification. This study uses Mini Digital Database for Screening Mammography (Mini-DDSM) breast dataset containing cancer and normal images. The mammography images are then improved using morphological erosion and enhanced using two image enhancement algorithms which are Unsharp Masking (UM) and High-Frequency Emphasis Filtering (HEF). Deep learning classification algorithms such as ResNet, DenseNet, and EfficientNet are employed to classify breast cancer. Each architecture is compared and analyzed regarding the effect of the image enhancement techniques and achieves the highest 76.08% accuracy score on breast cancer classification in the mammography dataset using the HEF technique.

AB - Mammography offers the most efficient approach for detecting breast illnesses early. Nevertheless, Image enhancement to improve breast cancer detection is required since mammograms are low-contrast and noisy images, and typical diagnostic markers such as microcalcifications and masses are challenging to identify. Due to this issue, this paper evaluates the impact of image enhancement on the performance of the deep learning approach and conducts qualitative and quantitative testing of various deep learning models in breast cancer classification. This study uses Mini Digital Database for Screening Mammography (Mini-DDSM) breast dataset containing cancer and normal images. The mammography images are then improved using morphological erosion and enhanced using two image enhancement algorithms which are Unsharp Masking (UM) and High-Frequency Emphasis Filtering (HEF). Deep learning classification algorithms such as ResNet, DenseNet, and EfficientNet are employed to classify breast cancer. Each architecture is compared and analyzed regarding the effect of the image enhancement techniques and achieves the highest 76.08% accuracy score on breast cancer classification in the mammography dataset using the HEF technique.

KW - High-Frequency Emphasis Filtering

KW - Unsharp Masking

KW - deep learning

KW - image enhancement

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U2 - 10.1109/ICCoSITE57641.2023.10127835

DO - 10.1109/ICCoSITE57641.2023.10127835

M3 - Conference contribution

AN - SCOPUS:85163099350

T3 - ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering: Digital Transformation Strategy in Facing the VUCA and TUNA Era

SP - 585

EP - 590

BT - ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 International Conference on Computer Science, Information Technology and Engineering, ICCoSITE 2023

Y2 - 16 February 2023

ER -

Kardawi MY, Sarno R. Image Enhancement for Breast Cancer Detection on Screening Mammography Using Deep Learning. In ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering: Digital Transformation Strategy in Facing the VUCA and TUNA Era. Institute of Electrical and Electronics Engineers Inc. 2023. p. 585-590. (ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering: Digital Transformation Strategy in Facing the VUCA and TUNA Era). doi: 10.1109/ICCoSITE57641.2023.10127835

Image Enhancement for Breast Cancer Detection on Screening Mammography Using Deep Learning

Abstract

Publication series

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UN SDGs

Keywords

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