Araştırma Makalesi
Yıl 2021,
Cilt: 7 Sayı: 1, 74 - 80, 30.04.2021
Öz
Kaynakça
- 1. Zhang X, Lin X, Zhang Z, Dong L, Sun X, Sun D, et al. Artificial intelligence medical ultrasound equipment: application of breast lesions detection. Ultrasonic Imaging. 2020;42(4-5):191-202.
- 2. Zhuang Z, Kang Y, Joseph Raj AN, Yuan Y, Ding W, Qiu S. Breast ultrasound lesion classification based on image decomposition and transfer learning. Medical Physics. 2020.
- 3. Hijab A, Rushdi MA, Gomaa MM, Eldeib A, editors. Breast cancer classification in ultrasound images using transfer learning. 2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME); 2019: IEEE.
- 4. Rodrigues PSJMD. Breast ultrasound image. 2017;1.
- 5. Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, et al. A comprehensive survey on transfer learning. 2020;109(1):43-76. 6. Cai C, Wang S, Xu Y, Zhang W, Tang K, Ouyang Q, et al. Transfer Learning for Drug Discovery. Journal of Medicinal Chemistry. 2020;63(16):8683-94.
- 7. Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al., editors. Teachable machine: Approachable Web-based tool for exploring machine learning classification. Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems; 2020.
- 8. YAŞAR Ş, ARSLAN A, Colak C, Yoloğlu SJMBSJoHS. A Developed Interactive Web Application for Statistical Analysis: Statistical Analysis Software.6(2):227-39.
- 9. Deniz E, Şengür A, Kadiroğlu Z, Guo Y, Bajaj V, Budak ÜJHis, et al. Transfer learning based histopathologic image classification for breast cancer detection. 2018;6(1):1-7.
- 10. Krizhevsky A, Sutskever I, Hinton GEJCotA. ImageNet classification with deep convolutional neural networks. 2017;60(6):84-90.
- 11. Abbasi AA, Hussain L, Awan IA, Abbasi I, Majid A, Nadeem MSA, et al. Detecting prostate cancer using deep learning convolution neural network with transfer learning approach. 2020;14(4):523-33.
- 12. Celik Y, Talo M, Yildirim O, Karabatak M, Acharya URJPRL. Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images. 2020;133:232-9.
- 13. Chaves E, Gonçalves CB, Albertini MK, Lee S, Jeon G, Fernandes HCJAO. Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images. 2020;59(17):E23-E8.
- 14. Khamparia A, Singh PK, Rani P, Samanta D, Khanna A, Bhushan BJToETT. An internet of health things‐driven deep learning framework for detection and classification of skin cancer using transfer learning. 2020:e3963.
- 15. Boumaraf S, Liu X, Zheng Z, Ma X, Ferkous CJBSP, Control. A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images. 2021;63:102192.
- 16. Khamparia A, Bharati S, Podder P, Gupta D, Khanna A, Phung TK, et al. Diagnosis of breast cancer based on modern mammography using hybrid transfer learning. 2021:1-19. 17. Rai R, Sisodia DS. Real-time data augmentation based transfer learning model for breast cancer diagnosis using histopathological images. Advances in Biomedical Engineering and Technology: Springer; 2021. p. 473-88.
- 18. Khan S, Islam N, Jan Z, Din IU, Rodrigues JJCJPRL. A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. 2019;125:1-6.
- 19. Senan EM, Alsaade FW, Al-mashhadani MIA, Theyazn H, Al-Adhaileh MHJJoAS, Engineering. Classification of Histopathological Images for Early Detection of Breast Cancer Using Deep Learning. 2021;24(3):323-9.
- 20. Bria A, Marrocco C, Tortorella FJCib, medicine. Addressing class imbalance in deep learning for small lesion detection on medical images. 2020;120:103735.
- 21. Zhang C, Tavanapong W, Kijkul G, Wong J, De Groen PC, Oh J, editors. Similarity-based active learning for image classification under class imbalance. 2018 IEEE International Conference on Data Mining (ICDM); 2018: IEEE.
- 22. Gao L, Zhang L, Liu C, Wu SJAIiM. Handling imbalanced medical image data: A deep-learning-based one-class classification approach. 2020;108:101935.
Yıl 2021,
Cilt: 7 Sayı: 1, 74 - 80, 30.04.2021
Öz
Objective: One of the most significant cancers impacting the health of women is breast cancer. This study aimed to provide breast cancer classification (benign and malignant) using the transfer learning method on the ultrasound images.
Methods: In the present study, a public imaging dataset was used for the breast cancer classification. Transfer learning technique was implemented for the detection and classification of breast cancer (benign or malignant) based on the ultrasound images. The current research includes data of 150 cases of malignant and 100 normal cases obtained from the Mendeley data. The relevant dataset was partitioned into training (85% of the images) and validation (15% of the images) sets. The present study implemented Teachable Machine (teachablemachine.withgoogle.com) for predicting the benign or malignant of breast cancer tumor based on the ultrasound images.
Results: According to the experimental results, accuracy, sensitivity and specificity with 95% confidence intervals were 0.974 (0.923-1.0), 0.957 (0.781-0.999) and 1 (0.782-1.0), respectively.
Conclusion: The model proposed in this study gave predictions that could be useful to clinicians in classifying breast cancer based on ultrasound images. Thus, this system can be developed in mobile, web, or alternative environments and offered as a computer-aided system for the use of radiologists, pathologists or other healthcare professionals in hospitals.
Anahtar Kelimeler
Breast Cancer Classification Ultrasound Images Transfer Learning
Kaynakça
- 1. Zhang X, Lin X, Zhang Z, Dong L, Sun X, Sun D, et al. Artificial intelligence medical ultrasound equipment: application of breast lesions detection. Ultrasonic Imaging. 2020;42(4-5):191-202.
- 2. Zhuang Z, Kang Y, Joseph Raj AN, Yuan Y, Ding W, Qiu S. Breast ultrasound lesion classification based on image decomposition and transfer learning. Medical Physics. 2020.
- 3. Hijab A, Rushdi MA, Gomaa MM, Eldeib A, editors. Breast cancer classification in ultrasound images using transfer learning. 2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME); 2019: IEEE.
- 4. Rodrigues PSJMD. Breast ultrasound image. 2017;1.
- 5. Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, et al. A comprehensive survey on transfer learning. 2020;109(1):43-76. 6. Cai C, Wang S, Xu Y, Zhang W, Tang K, Ouyang Q, et al. Transfer Learning for Drug Discovery. Journal of Medicinal Chemistry. 2020;63(16):8683-94.
- 7. Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al., editors. Teachable machine: Approachable Web-based tool for exploring machine learning classification. Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems; 2020.
- 8. YAŞAR Ş, ARSLAN A, Colak C, Yoloğlu SJMBSJoHS. A Developed Interactive Web Application for Statistical Analysis: Statistical Analysis Software.6(2):227-39.
- 9. Deniz E, Şengür A, Kadiroğlu Z, Guo Y, Bajaj V, Budak ÜJHis, et al. Transfer learning based histopathologic image classification for breast cancer detection. 2018;6(1):1-7.
- 10. Krizhevsky A, Sutskever I, Hinton GEJCotA. ImageNet classification with deep convolutional neural networks. 2017;60(6):84-90.
- 11. Abbasi AA, Hussain L, Awan IA, Abbasi I, Majid A, Nadeem MSA, et al. Detecting prostate cancer using deep learning convolution neural network with transfer learning approach. 2020;14(4):523-33.
- 12. Celik Y, Talo M, Yildirim O, Karabatak M, Acharya URJPRL. Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images. 2020;133:232-9.
- 13. Chaves E, Gonçalves CB, Albertini MK, Lee S, Jeon G, Fernandes HCJAO. Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images. 2020;59(17):E23-E8.
- 14. Khamparia A, Singh PK, Rani P, Samanta D, Khanna A, Bhushan BJToETT. An internet of health things‐driven deep learning framework for detection and classification of skin cancer using transfer learning. 2020:e3963.
- 15. Boumaraf S, Liu X, Zheng Z, Ma X, Ferkous CJBSP, Control. A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images. 2021;63:102192.
- 16. Khamparia A, Bharati S, Podder P, Gupta D, Khanna A, Phung TK, et al. Diagnosis of breast cancer based on modern mammography using hybrid transfer learning. 2021:1-19. 17. Rai R, Sisodia DS. Real-time data augmentation based transfer learning model for breast cancer diagnosis using histopathological images. Advances in Biomedical Engineering and Technology: Springer; 2021. p. 473-88.
- 18. Khan S, Islam N, Jan Z, Din IU, Rodrigues JJCJPRL. A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. 2019;125:1-6.
- 19. Senan EM, Alsaade FW, Al-mashhadani MIA, Theyazn H, Al-Adhaileh MHJJoAS, Engineering. Classification of Histopathological Images for Early Detection of Breast Cancer Using Deep Learning. 2021;24(3):323-9.
- 20. Bria A, Marrocco C, Tortorella FJCib, medicine. Addressing class imbalance in deep learning for small lesion detection on medical images. 2020;120:103735.
- 21. Zhang C, Tavanapong W, Kijkul G, Wong J, De Groen PC, Oh J, editors. Similarity-based active learning for image classification under class imbalance. 2018 IEEE International Conference on Data Mining (ICDM); 2018: IEEE.
- 22. Gao L, Zhang L, Liu C, Wu SJAIiM. Handling imbalanced medical image data: A deep-learning-based one-class classification approach. 2020;108:101935.
Toplam 20 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Sağlık Kurumları Yönetimi |
| Bölüm | Araştırma Makaleleri |
| Yazarlar | |
| Yayımlanma Tarihi | 30 Nisan 2021 |
| Yayımlandığı Sayı | Yıl 2021 Cilt: 7 Sayı: 1 |
