Enhancing Ductal Carcinoma Classification Using Transfer Learning with 3D U-Net Models in Breast Cancer Imaging
Breast cancer ranks among the leading causes of death for women globally, making it imperative to swiftly and precisely detect the condition to ensure timely treatment and enhanced chances of recovery. This study focuses on transfer learning with 3D U-Net models to classify ductal carcinoma, the mos...
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/7/4255 |
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| author | Saman Khalil Uroosa Nawaz Zubariah Zohaib Mushtaq Saad Arif Muhammad Zia ur Rehman Muhammad Farrukh Qureshi Abdul Malik Adham Aleid Khalid Alhussaini |
| author_facet | Saman Khalil Uroosa Nawaz Zubariah Zohaib Mushtaq Saad Arif Muhammad Zia ur Rehman Muhammad Farrukh Qureshi Abdul Malik Adham Aleid Khalid Alhussaini |
| author_sort | Saman Khalil |
| collection | DOAJ |
| description | Breast cancer ranks among the leading causes of death for women globally, making it imperative to swiftly and precisely detect the condition to ensure timely treatment and enhanced chances of recovery. This study focuses on transfer learning with 3D U-Net models to classify ductal carcinoma, the most frequent subtype of breast cancer, in histopathology imaging. In this research work, a dataset of 162 microscopic images of breast cancer specimens is utilized for breast histopathology analysis. Preprocessing the original image data includes shrinking the images, standardizing the intensities, and extracting patches of size 50 × 50 pixels. The retrieved patches were employed to construct a basic 3D U-Net model and a refined 3D U-Net model that had been previously trained on an extensive medical image segmentation dataset. The findings revealed that the fine-tuned 3D U-Net model (97%) outperformed the simple 3D U-Net model (87%) in identifying ductal cancer in breast histopathology imaging. The fine-tuned model exhibited a smaller loss (0.003) on the testing data (0.041) in comparison to the simple model. The disparity in the training and testing accuracy reveals that the fine-tuned model may have overfitted to the training data indicating that there is room for improvement. To progress in computer-aided diagnosis, the research study also adopted various data augmentation methodologies. The experimental approach that was put forward achieved state-of-the-art performance, surpassing the benchmark techniques used in previous studies in the same field, and exhibiting greater accuracy. The presented scheme has promising potential for better cancer detection and diagnosis in practical applications of mammography. |
| first_indexed | 2024-03-11T05:42:36Z |
| format | Article |
| id | doaj.art-23fad0d5cb774512b99d74ea62738a2e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:42:36Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-23fad0d5cb774512b99d74ea62738a2e2023-11-17T16:17:45ZengMDPI AGApplied Sciences2076-34172023-03-01137425510.3390/app13074255Enhancing Ductal Carcinoma Classification Using Transfer Learning with 3D U-Net Models in Breast Cancer ImagingSaman Khalil0Uroosa Nawaz1Zubariah2Zohaib Mushtaq3Saad Arif4Muhammad Zia ur Rehman5Muhammad Farrukh Qureshi6Abdul Malik7Adham Aleid8Khalid Alhussaini9Rural Health Centre, Moazamabad, Sargodha 40100, PakistanBasic Health Unit, Gulial, Jand, Attock 43600, PakistanIsfandyar Bukhari District Headquarters Hospital, Attock 43600, PakistanDepartment of Electrical Engineering, College of Engineering and Technology, University of Sargodha, Sargodha 40100, PakistanDepartment of Mechanical Engineering, HITEC University, Taxila 47080, PakistanDepartment of Biomedical Engineering, Riphah International University, Islamabad 44000, PakistanDepartment of Electrical Engineering, Riphah International University, Islamabad 44000, PakistanDepartment of Biomedical Engineering, Riphah International University, Islamabad 44000, PakistanDepartment of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 12372, Saudi ArabiaDepartment of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 12372, Saudi ArabiaBreast cancer ranks among the leading causes of death for women globally, making it imperative to swiftly and precisely detect the condition to ensure timely treatment and enhanced chances of recovery. This study focuses on transfer learning with 3D U-Net models to classify ductal carcinoma, the most frequent subtype of breast cancer, in histopathology imaging. In this research work, a dataset of 162 microscopic images of breast cancer specimens is utilized for breast histopathology analysis. Preprocessing the original image data includes shrinking the images, standardizing the intensities, and extracting patches of size 50 × 50 pixels. The retrieved patches were employed to construct a basic 3D U-Net model and a refined 3D U-Net model that had been previously trained on an extensive medical image segmentation dataset. The findings revealed that the fine-tuned 3D U-Net model (97%) outperformed the simple 3D U-Net model (87%) in identifying ductal cancer in breast histopathology imaging. The fine-tuned model exhibited a smaller loss (0.003) on the testing data (0.041) in comparison to the simple model. The disparity in the training and testing accuracy reveals that the fine-tuned model may have overfitted to the training data indicating that there is room for improvement. To progress in computer-aided diagnosis, the research study also adopted various data augmentation methodologies. The experimental approach that was put forward achieved state-of-the-art performance, surpassing the benchmark techniques used in previous studies in the same field, and exhibiting greater accuracy. The presented scheme has promising potential for better cancer detection and diagnosis in practical applications of mammography.https://www.mdpi.com/2076-3417/13/7/4255ductal carcinomabreast cancer detectionMRItransfer learningU-Netsintelligent healthcare |
| spellingShingle | Saman Khalil Uroosa Nawaz Zubariah Zohaib Mushtaq Saad Arif Muhammad Zia ur Rehman Muhammad Farrukh Qureshi Abdul Malik Adham Aleid Khalid Alhussaini Enhancing Ductal Carcinoma Classification Using Transfer Learning with 3D U-Net Models in Breast Cancer Imaging Applied Sciences ductal carcinoma breast cancer detection MRI transfer learning U-Nets intelligent healthcare |
| title | Enhancing Ductal Carcinoma Classification Using Transfer Learning with 3D U-Net Models in Breast Cancer Imaging |
| title_full | Enhancing Ductal Carcinoma Classification Using Transfer Learning with 3D U-Net Models in Breast Cancer Imaging |
| title_fullStr | Enhancing Ductal Carcinoma Classification Using Transfer Learning with 3D U-Net Models in Breast Cancer Imaging |
| title_full_unstemmed | Enhancing Ductal Carcinoma Classification Using Transfer Learning with 3D U-Net Models in Breast Cancer Imaging |
| title_short | Enhancing Ductal Carcinoma Classification Using Transfer Learning with 3D U-Net Models in Breast Cancer Imaging |
| title_sort | enhancing ductal carcinoma classification using transfer learning with 3d u net models in breast cancer imaging |
| topic | ductal carcinoma breast cancer detection MRI transfer learning U-Nets intelligent healthcare |
| url | https://www.mdpi.com/2076-3417/13/7/4255 |
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