Enhancing the Quality and Authenticity of Synthetic Mammogram Images for Improved Breast Cancer Detection
Breast cancer is widespread throughout the world and can be cured if diagnosed early. Mammography is an irreplaceable and critical technique in modern medicine that serves as a foundation for the detection of breast cancer. In medical imaging, the reliability of synthetic mammogram images is produce...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10401230/ |
| _version_ | 1797346328740626432 |
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| author | Dilawar Shah Mohammad Asmat Ullah Khan Mohammad Abrar Farhan Amin Bader Fahad Alkhamees Hussain AlSalman |
| author_facet | Dilawar Shah Mohammad Asmat Ullah Khan Mohammad Abrar Farhan Amin Bader Fahad Alkhamees Hussain AlSalman |
| author_sort | Dilawar Shah |
| collection | DOAJ |
| description | Breast cancer is widespread throughout the world and can be cured if diagnosed early. Mammography is an irreplaceable and critical technique in modern medicine that serves as a foundation for the detection of breast cancer. In medical imaging, the reliability of synthetic mammogram images is produced by deep convolutional generative adversarial networks (DCGAN). Human validation to assess the quality of synthetic images to examine and calculate the perceptual variations between synthetic images and their real-world counterparts is a difficult task. Thus, this research focused on improving the quality and authenticity of synthetic mammogram images. For this, we explored and identified a new research gap because radiologists consistently expressed much higher confidence levels in real mammogram images in their assessment process. This research highlights the key difference between synthetic and real mammograms by defining mean scores. The defined mean identifies a large gap, with real mammographic images receiving an average score of 0.73 and a synthetic score of 0.31. A statistical analysis was performed, which produced a T-statistic of -6.35, a p-value less than 0.001, and a 95% confidence interval ranging from -0.50 to -0.28. These results have a wide range of implications. It emphasizes the urgent need for further improvements in the generative model, improving the legitimacy and caliber of synthetic mammogram images. Our research highlights how crucial it is to incorporate synthetic images into clinical practice with caution and thought. Ethical considerations must encompass the potential consequences of relying on synthetic data in medical decision-making, along with concerns related to diagnostic accuracy and patient safety. |
| first_indexed | 2024-03-08T11:31:30Z |
| format | Article |
| id | doaj.art-df3a7c3b28554963bcec7eb3e091a4a4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T11:31:30Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-df3a7c3b28554963bcec7eb3e091a4a42024-01-26T00:01:29ZengIEEEIEEE Access2169-35362024-01-0112121891219810.1109/ACCESS.2024.335482610401230Enhancing the Quality and Authenticity of Synthetic Mammogram Images for Improved Breast Cancer DetectionDilawar Shah0https://orcid.org/0000-0003-2701-6646Mohammad Asmat Ullah Khan1Mohammad Abrar2Farhan Amin3https://orcid.org/0000-0002-6385-5511Bader Fahad Alkhamees4https://orcid.org/0000-0001-7479-7102Hussain AlSalman5https://orcid.org/0000-0001-8172-4964Department of Computer Science, International Islamic University, Islamabad, Islamabad, PakistanDepartment of Computer Science, International Islamic University, Islamabad, Islamabad, PakistanDepartment of Computer Science, Bacha Khan University, Chārsadda, PakistanDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of KoreaDepartment of Information Systems, College of Computer and Information Science, King Saud University, Riyadh, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaBreast cancer is widespread throughout the world and can be cured if diagnosed early. Mammography is an irreplaceable and critical technique in modern medicine that serves as a foundation for the detection of breast cancer. In medical imaging, the reliability of synthetic mammogram images is produced by deep convolutional generative adversarial networks (DCGAN). Human validation to assess the quality of synthetic images to examine and calculate the perceptual variations between synthetic images and their real-world counterparts is a difficult task. Thus, this research focused on improving the quality and authenticity of synthetic mammogram images. For this, we explored and identified a new research gap because radiologists consistently expressed much higher confidence levels in real mammogram images in their assessment process. This research highlights the key difference between synthetic and real mammograms by defining mean scores. The defined mean identifies a large gap, with real mammographic images receiving an average score of 0.73 and a synthetic score of 0.31. A statistical analysis was performed, which produced a T-statistic of -6.35, a p-value less than 0.001, and a 95% confidence interval ranging from -0.50 to -0.28. These results have a wide range of implications. It emphasizes the urgent need for further improvements in the generative model, improving the legitimacy and caliber of synthetic mammogram images. Our research highlights how crucial it is to incorporate synthetic images into clinical practice with caution and thought. Ethical considerations must encompass the potential consequences of relying on synthetic data in medical decision-making, along with concerns related to diagnostic accuracy and patient safety.https://ieeexplore.ieee.org/document/10401230/Breast cancercomputer-aided diagnosisdeep learninggenerative modelsmedical imagingmedical diagnosis |
| spellingShingle | Dilawar Shah Mohammad Asmat Ullah Khan Mohammad Abrar Farhan Amin Bader Fahad Alkhamees Hussain AlSalman Enhancing the Quality and Authenticity of Synthetic Mammogram Images for Improved Breast Cancer Detection IEEE Access Breast cancer computer-aided diagnosis deep learning generative models medical imaging medical diagnosis |
| title | Enhancing the Quality and Authenticity of Synthetic Mammogram Images for Improved Breast Cancer Detection |
| title_full | Enhancing the Quality and Authenticity of Synthetic Mammogram Images for Improved Breast Cancer Detection |
| title_fullStr | Enhancing the Quality and Authenticity of Synthetic Mammogram Images for Improved Breast Cancer Detection |
| title_full_unstemmed | Enhancing the Quality and Authenticity of Synthetic Mammogram Images for Improved Breast Cancer Detection |
| title_short | Enhancing the Quality and Authenticity of Synthetic Mammogram Images for Improved Breast Cancer Detection |
| title_sort | enhancing the quality and authenticity of synthetic mammogram images for improved breast cancer detection |
| topic | Breast cancer computer-aided diagnosis deep learning generative models medical imaging medical diagnosis |
| url | https://ieeexplore.ieee.org/document/10401230/ |
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