Enhancing Mammography Models: The Impact of Radiologist Recommendations on Algorithmic Precision
Keywords:
Breast Cancer, Machine learning in radiology, Medical image classification, Mammography, Radiology image classification
Abstract
This study highlights the benefits of advanced image classification in breast cancer diagnosis and treatment. We utilize deep learning algorithms like YOLOv5 for image segmentation and Densenet121 for feature extraction from segmented regions. Our dataset includes 54,706 mammography images for comprehensive analysis. We evaluate 100 challenging cases, ensuring a balanced representation of benign and malignant instances. Validation involves 50 consensus cases. To address the class imbalance, we employ Upsampling/Downsampling. We fine-tune 14 algorithms and compare outcomes with and without radiologists' recommendations. Results show a 99.8\% AUC during testing and 59.5\% during validation without radiologists' input, which improves to 99.9\% and 93.5\% respectively with their insights. Expert guidance significantly enhances diagnostic accuracy. The study explores the interplay between algorithmic precision, dataset characteristics, and expert recommendations in breast cancer diagnosis. It provides valuable insights for leveraging technology and expert knowledge for improved medical outcomes.References
1. Saad Albawi, Tareq Abed Mohammed, and Saad Al-Zawi. Understanding of a convolutional neural network. In 2017 international conference on engineering and technology (ICET), pages 1–6. Ieee, 2017.
2. Syed M Anwar and Ulas Bagci. Artifcial intelligence as another set of eyes in breast cancer diagnosis. Journal of Medical Artifcial Intelligence, 2(May):10, 2019.
3. Philippe Autier and Mathieu Boniol. Mammography screening: A major issue in medicine. European journal of cancer, 90:34–62, 2018.
4. Leonard Berlin. Relying on the radiologist. American Journal of Roentgenology, 179(1):43–46, 2002.
5. FTH Bodewes, AA van Asselt, MD Dorrius, MJW Greuter, and GH de Bock. Mammographic breast density and the risk of breastcancer: A systematic review and meta-analysis. The Breast, 2022.
6. Sabri Boughorbel, Fethi Jarray, and Mohammed El-Anbari. Optimal classifer for imbalanced data using matthews correlation coeffcient metric. PloS one, 12(6):e0177678, 2017.
7. Wei Cao, Hong-Da Chen, Yi-Wen Yu, Ni Li, and Wan-Qing Chen. Changing profles of cancer burden worldwide and in china: a secondary analysis of the global cancer statistics 2020. Chinese medical journal, 134(07):783–791, 2021.
8. Gustavo Carneiro, Jacinto Nascimento, and Andrew P Bradley. Automated analysis of unregistered multi-view mammograms with deep learning. IEEE transactions on medical imaging, 36(11):2355–2365, 2017.
9. V Cheplygina. Lessons from shortcomings in machine learning for medical imaging. Artifcial Intelligence in Science, page 238.
10. Hiba Chougrad, Hamid Zouaki, and Omar Alheyane. Deep convolutional neural networks for breast cancer screening. Computer methods and programs in biomedicine, 157:19–30, 2018.
11. Rob A Dunne and Norm A Campbell. On the pairing of the softmax activation and cross-entropy penalty functions and the derivation of the softmax activation function. In Proc. 8th Aust. Conf. on the Neural Networks, Melbourne, volume 181, page 185. Citeseer, 1997.
12. NM Faber and R Rajko. How to avoid over-ftting in multivariate calibration—the conventional validation approach and an alternative. Analytica Chimica Acta, 595(1-2):98–106, 2007.
13. Yiming Fang, Xianxin Guo, Kun Chen, Zhu Zhou, and Qing Ye. Accurate and automated detection of surface knots on sawn timbers using yolo-v5 model. BioResources, 16(3), 2021.
14. Alberto Fernandez, Salvador Garcia, Francisco Herrera, and Nitesh V Chawla. Smote for learning from imbalanced data: progress ´ and challenges, marking the 15-year anniversary. Journal of artifcial intelligence research, 61:863–905, 2018.
15. Ross Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 1440–1448, 2015.
16. Yoichi Hayashi. Toward the transparency of deep learning in radiological imaging: Beyond quantitative to qualitative artifcial intelligence. Journal of Medical Artifcial Intelligence, 2, 2019.
17. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
18. Michael Heath, Kevin Bowyer, Daniel Kopans, P Kegelmeyer Jr, Richard Moore, Kyong Chang, and S Munishkumaran. Current status of the digital database for screening mammography. In Digital Mammography: Nijmegen, 1998, pages 457–460. Springer, 1998.
19. Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H Schwartz, and Hugo JWL Aerts. Artifcial intelligence in radiology. Nature Reviews Cancer, 18(8):500–510, 2018.
20. Yuzhou Hu, Yi Guo, Yuanyuan Wang, Jinhua Yu, Jiawei Li, Shichong Zhou, and Cai Chang. Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model. Medical physics, 46(1):215–228, 2019.
21. Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708, 2017.
22. Kristina Ionkina, Andrey Svistunov, Ilya Galin, Boris Onykiy, and Larisa Pronicheva. Mias database semantic structure. Procedia computer science, 145:254–259, 2018.
23. Nathalie Japkowicz and Shaju Stephen. The class imbalance problem: A systematic study. Intelligent data analysis, 6(5):429–449, 2002.
24. Hwejin Jung, Bumsoo Kim, Inyeop Lee, Minhwan Yoo, Junhyun Lee, Sooyoun Ham, Okhee Woo, and Jaewoo Kang. Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PloS one, 13(9):e0203355, 2018.
25. Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. Lightgbm: A highly effcient gradient boosting decision tree. Advances in neural information processing systems, 30, 2017.
26. Daniel B Kopans. Breast imaging. Lippincott Williams & Wilkins, 2007.
27. Bartosz Krawczyk. Learning from imbalanced data: open challenges and future directions. Progress in Artifcial Intelligence, 5(4):221–232, 2016.
28. Naresh Kumar, Manoj Sharma, Vijay Pal Singh, Charanjeet Madan, and Seema Mehandia. An empirical study of handcrafted and dense feature extraction techniques for lung and colon cancer classifcation from histopathological images. Biomedical Signal Processing and Control, 75:103596, 2022.
29. Hai-Son Le, Ilya Oparin, Alexandre Allauzen, Jean-Luc Gauvain, and Franc¸ois Yvon. Structured output layer neural network language model. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5524–5527. IEEE, 2011.
30. Rebecca Sawyer Lee, Francisco Gimenez, Assaf Hoogi, Kanae Kawai Miyake, Mia Gorovoy, and Daniel L Rubin. A curated mammography data set for use in computer-aided detection and diagnosis research. Scientifc data, 4(1):1–9, 2017.
31. Petro Liashchynskyi and Pavlo Liashchynskyi. Grid search, random search, genetic algorithm: a big comparison for nas. arXiv preprint arXiv:1912.06059, 2019.
32. Miguel G Lopez, Naimy Posada, Daniel C Moura, Raul Ramos Poll ´ an, Jos ´ e M Franco Valiente, C ´ esar Su ´ arez Ortega, Manuel ´ Solar, Guillermo Diaz-Herrero, IMAP Ramos, Joana Loureiro, et al. Bcdr: a breast cancer digital repository. In 15th International conference on experimental mechanics, volume 1215, pages 113–120, 2012
33. Aqsa Mohiyuddin, Asma Basharat, Usman Ghani, Vesely Peter, Sidra Abbas, Osama Bin Naeem, and Muhammad Rizwan. Breast ` tumor detection and classifcation in mammogram images using modifed yolov5 network. Computational and Mathematical Methods in Medicine, 2022:1–16, 2022.
34. Ines C Moreira, Igor Amaral, In ˆ es Domingues, Ant ˆ onio Cardoso, Maria Joao Cardoso, and Jaime S Cardoso. Inbreast: toward a full-feld digital mammographic database. Academic radiology, 19(2):236–248, 2012.
35. FY Osisanwo, JET Akinsola, O Awodele, JO Hinmikaiye, O Olakanmi, J Akinjobi, et al. Supervised machine learning algorithms: classifcation and comparison. International Journal of Computer Trends and Technology (IJCTT), 48(3):128–138, 2017.
36. SGOPAL Patro and Kishore Kumar Sahu. Normalization: A preprocessing stage. arXiv preprint arXiv:1503.06462, 2015.
37. Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter ¨Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in python. the Journal of machine Learning research, 12:2825–2830, 2011.
38. AC Pierrefeu-Lagrange, E Delay, N Guerin, K Chekaroua, and T Delaporte. Evaluation radiologique des seins reconstruits ayant ´ ben ´ efci ´ e d’un lipomodelage. In ´ Annales de chirurgie plastique esthetique, volume 51, pages 18–28. Elsevier, 2006.
39. David MW Powers. Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061, 2020.
40. Hameedur Rahman, Tanvir Fatima Naik Bukht, Rozilawati Ahmad, Ahmad Almadhor, Abdul Rehman Javed, et al. Effcient breast cancer diagnosis from complex mammographic images using deep convolutional neural network. Computational intelligence and neuroscience, 2023, 2023.
41. Waseem Rawat and Zenghui Wang. Deep convolutional neural networks for image classifcation : A comprehensive review. Neural computation, 29(9):2352–2449, 2017.
42. Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018.
43. Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 234–241. Springer, 2015.
44. Wessam M Salama and Moustafa H Aly. Deep learning in mammography images segmentation and classifcation: Automated cnn approach. Alexandria Engineering Journal, 60(5):4701–4709, 2021.
45. Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4510–4520, 2018.
46. Li Shen, Laurie R Margolies, Joseph H Rothstein, Eugene Fluder, Russell McBride, and Weiva Sieh. Deep learning to improve breast cancer detection on screening mammography. Scientifc reports, 9(1):12495, 2019.
47. Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
48. Keri Stephens. Rsna announces launch of screening mammography breast cancer detection ai challenge. AXIS Imaging News, 2022.
49. Yong Joon Suh, Jaewon Jung, and Bum-Joo Cho. Automated breast cancer detection in digital mammograms of various densities via deep learning. Journal of personalized medicine, 10(4):211, 2020.
50. Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826, 2016.
51. Wil MP Van der Aalst, Vladimir Rubin, HMW Verbeek, Boudewijn F van Dongen, Ekkart Kindler, and Christian W Gunther. Process ¨ mining: a two-step approach to balance between underftting and overftting. Software & Systems Modeling, 9:87–111, 2010.
52. A Venmathi, E Ganesh, and N Kumaratharan. A review of medical image classifcation and evaluation methodology for breast cancer diagnosis with computer aided mammography. Int’l Journal of Applied Engineering Research, 10(11):30045–30054, 2015.
53. Sofa Visa, Brian Ramsay, Anca L Ralescu, and Esther Van Der Knaap. Confusion matrix-based feature selection. Maics, 710(1):120–127, 2011.
54. Kelei Wang and Juncheng Wei. Second order estimate on transition layers. Advances in Mathematics, 358:106856, 2019.
55. Donald L Weaver, Robert D Rosenberg, William E Barlow, Laura Ichikawa, Patricia A Carney, Karla Kerlikowske, Diana SM Buist, Berta M Geller, Charles R Key, Susan J Maygarden, et al. Pathologic fndings from the breast cancer surveillance consortium: population-based outcomes in women undergoing biopsy after screening mammography. Cancer, 106(4):732–742, 2006.
56. Baizheng Wu, Chengxin Pang, Xinhua Zeng, and Xing Hu. Me-yolo: Improved yolov5 for detecting medical personal protective equipment. Applied Sciences, 12(23):11978, 2022.
57. Li Yang and Abdallah Shami. On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 415:295–316, 2020
2. Syed M Anwar and Ulas Bagci. Artifcial intelligence as another set of eyes in breast cancer diagnosis. Journal of Medical Artifcial Intelligence, 2(May):10, 2019.
3. Philippe Autier and Mathieu Boniol. Mammography screening: A major issue in medicine. European journal of cancer, 90:34–62, 2018.
4. Leonard Berlin. Relying on the radiologist. American Journal of Roentgenology, 179(1):43–46, 2002.
5. FTH Bodewes, AA van Asselt, MD Dorrius, MJW Greuter, and GH de Bock. Mammographic breast density and the risk of breastcancer: A systematic review and meta-analysis. The Breast, 2022.
6. Sabri Boughorbel, Fethi Jarray, and Mohammed El-Anbari. Optimal classifer for imbalanced data using matthews correlation coeffcient metric. PloS one, 12(6):e0177678, 2017.
7. Wei Cao, Hong-Da Chen, Yi-Wen Yu, Ni Li, and Wan-Qing Chen. Changing profles of cancer burden worldwide and in china: a secondary analysis of the global cancer statistics 2020. Chinese medical journal, 134(07):783–791, 2021.
8. Gustavo Carneiro, Jacinto Nascimento, and Andrew P Bradley. Automated analysis of unregistered multi-view mammograms with deep learning. IEEE transactions on medical imaging, 36(11):2355–2365, 2017.
9. V Cheplygina. Lessons from shortcomings in machine learning for medical imaging. Artifcial Intelligence in Science, page 238.
10. Hiba Chougrad, Hamid Zouaki, and Omar Alheyane. Deep convolutional neural networks for breast cancer screening. Computer methods and programs in biomedicine, 157:19–30, 2018.
11. Rob A Dunne and Norm A Campbell. On the pairing of the softmax activation and cross-entropy penalty functions and the derivation of the softmax activation function. In Proc. 8th Aust. Conf. on the Neural Networks, Melbourne, volume 181, page 185. Citeseer, 1997.
12. NM Faber and R Rajko. How to avoid over-ftting in multivariate calibration—the conventional validation approach and an alternative. Analytica Chimica Acta, 595(1-2):98–106, 2007.
13. Yiming Fang, Xianxin Guo, Kun Chen, Zhu Zhou, and Qing Ye. Accurate and automated detection of surface knots on sawn timbers using yolo-v5 model. BioResources, 16(3), 2021.
14. Alberto Fernandez, Salvador Garcia, Francisco Herrera, and Nitesh V Chawla. Smote for learning from imbalanced data: progress ´ and challenges, marking the 15-year anniversary. Journal of artifcial intelligence research, 61:863–905, 2018.
15. Ross Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 1440–1448, 2015.
16. Yoichi Hayashi. Toward the transparency of deep learning in radiological imaging: Beyond quantitative to qualitative artifcial intelligence. Journal of Medical Artifcial Intelligence, 2, 2019.
17. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
18. Michael Heath, Kevin Bowyer, Daniel Kopans, P Kegelmeyer Jr, Richard Moore, Kyong Chang, and S Munishkumaran. Current status of the digital database for screening mammography. In Digital Mammography: Nijmegen, 1998, pages 457–460. Springer, 1998.
19. Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H Schwartz, and Hugo JWL Aerts. Artifcial intelligence in radiology. Nature Reviews Cancer, 18(8):500–510, 2018.
20. Yuzhou Hu, Yi Guo, Yuanyuan Wang, Jinhua Yu, Jiawei Li, Shichong Zhou, and Cai Chang. Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model. Medical physics, 46(1):215–228, 2019.
21. Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708, 2017.
22. Kristina Ionkina, Andrey Svistunov, Ilya Galin, Boris Onykiy, and Larisa Pronicheva. Mias database semantic structure. Procedia computer science, 145:254–259, 2018.
23. Nathalie Japkowicz and Shaju Stephen. The class imbalance problem: A systematic study. Intelligent data analysis, 6(5):429–449, 2002.
24. Hwejin Jung, Bumsoo Kim, Inyeop Lee, Minhwan Yoo, Junhyun Lee, Sooyoun Ham, Okhee Woo, and Jaewoo Kang. Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PloS one, 13(9):e0203355, 2018.
25. Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. Lightgbm: A highly effcient gradient boosting decision tree. Advances in neural information processing systems, 30, 2017.
26. Daniel B Kopans. Breast imaging. Lippincott Williams & Wilkins, 2007.
27. Bartosz Krawczyk. Learning from imbalanced data: open challenges and future directions. Progress in Artifcial Intelligence, 5(4):221–232, 2016.
28. Naresh Kumar, Manoj Sharma, Vijay Pal Singh, Charanjeet Madan, and Seema Mehandia. An empirical study of handcrafted and dense feature extraction techniques for lung and colon cancer classifcation from histopathological images. Biomedical Signal Processing and Control, 75:103596, 2022.
29. Hai-Son Le, Ilya Oparin, Alexandre Allauzen, Jean-Luc Gauvain, and Franc¸ois Yvon. Structured output layer neural network language model. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5524–5527. IEEE, 2011.
30. Rebecca Sawyer Lee, Francisco Gimenez, Assaf Hoogi, Kanae Kawai Miyake, Mia Gorovoy, and Daniel L Rubin. A curated mammography data set for use in computer-aided detection and diagnosis research. Scientifc data, 4(1):1–9, 2017.
31. Petro Liashchynskyi and Pavlo Liashchynskyi. Grid search, random search, genetic algorithm: a big comparison for nas. arXiv preprint arXiv:1912.06059, 2019.
32. Miguel G Lopez, Naimy Posada, Daniel C Moura, Raul Ramos Poll ´ an, Jos ´ e M Franco Valiente, C ´ esar Su ´ arez Ortega, Manuel ´ Solar, Guillermo Diaz-Herrero, IMAP Ramos, Joana Loureiro, et al. Bcdr: a breast cancer digital repository. In 15th International conference on experimental mechanics, volume 1215, pages 113–120, 2012
33. Aqsa Mohiyuddin, Asma Basharat, Usman Ghani, Vesely Peter, Sidra Abbas, Osama Bin Naeem, and Muhammad Rizwan. Breast ` tumor detection and classifcation in mammogram images using modifed yolov5 network. Computational and Mathematical Methods in Medicine, 2022:1–16, 2022.
34. Ines C Moreira, Igor Amaral, In ˆ es Domingues, Ant ˆ onio Cardoso, Maria Joao Cardoso, and Jaime S Cardoso. Inbreast: toward a full-feld digital mammographic database. Academic radiology, 19(2):236–248, 2012.
35. FY Osisanwo, JET Akinsola, O Awodele, JO Hinmikaiye, O Olakanmi, J Akinjobi, et al. Supervised machine learning algorithms: classifcation and comparison. International Journal of Computer Trends and Technology (IJCTT), 48(3):128–138, 2017.
36. SGOPAL Patro and Kishore Kumar Sahu. Normalization: A preprocessing stage. arXiv preprint arXiv:1503.06462, 2015.
37. Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter ¨Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in python. the Journal of machine Learning research, 12:2825–2830, 2011.
38. AC Pierrefeu-Lagrange, E Delay, N Guerin, K Chekaroua, and T Delaporte. Evaluation radiologique des seins reconstruits ayant ´ ben ´ efci ´ e d’un lipomodelage. In ´ Annales de chirurgie plastique esthetique, volume 51, pages 18–28. Elsevier, 2006.
39. David MW Powers. Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061, 2020.
40. Hameedur Rahman, Tanvir Fatima Naik Bukht, Rozilawati Ahmad, Ahmad Almadhor, Abdul Rehman Javed, et al. Effcient breast cancer diagnosis from complex mammographic images using deep convolutional neural network. Computational intelligence and neuroscience, 2023, 2023.
41. Waseem Rawat and Zenghui Wang. Deep convolutional neural networks for image classifcation : A comprehensive review. Neural computation, 29(9):2352–2449, 2017.
42. Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018.
43. Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 234–241. Springer, 2015.
44. Wessam M Salama and Moustafa H Aly. Deep learning in mammography images segmentation and classifcation: Automated cnn approach. Alexandria Engineering Journal, 60(5):4701–4709, 2021.
45. Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4510–4520, 2018.
46. Li Shen, Laurie R Margolies, Joseph H Rothstein, Eugene Fluder, Russell McBride, and Weiva Sieh. Deep learning to improve breast cancer detection on screening mammography. Scientifc reports, 9(1):12495, 2019.
47. Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
48. Keri Stephens. Rsna announces launch of screening mammography breast cancer detection ai challenge. AXIS Imaging News, 2022.
49. Yong Joon Suh, Jaewon Jung, and Bum-Joo Cho. Automated breast cancer detection in digital mammograms of various densities via deep learning. Journal of personalized medicine, 10(4):211, 2020.
50. Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826, 2016.
51. Wil MP Van der Aalst, Vladimir Rubin, HMW Verbeek, Boudewijn F van Dongen, Ekkart Kindler, and Christian W Gunther. Process ¨ mining: a two-step approach to balance between underftting and overftting. Software & Systems Modeling, 9:87–111, 2010.
52. A Venmathi, E Ganesh, and N Kumaratharan. A review of medical image classifcation and evaluation methodology for breast cancer diagnosis with computer aided mammography. Int’l Journal of Applied Engineering Research, 10(11):30045–30054, 2015.
53. Sofa Visa, Brian Ramsay, Anca L Ralescu, and Esther Van Der Knaap. Confusion matrix-based feature selection. Maics, 710(1):120–127, 2011.
54. Kelei Wang and Juncheng Wei. Second order estimate on transition layers. Advances in Mathematics, 358:106856, 2019.
55. Donald L Weaver, Robert D Rosenberg, William E Barlow, Laura Ichikawa, Patricia A Carney, Karla Kerlikowske, Diana SM Buist, Berta M Geller, Charles R Key, Susan J Maygarden, et al. Pathologic fndings from the breast cancer surveillance consortium: population-based outcomes in women undergoing biopsy after screening mammography. Cancer, 106(4):732–742, 2006.
56. Baizheng Wu, Chengxin Pang, Xinhua Zeng, and Xing Hu. Me-yolo: Improved yolov5 for detecting medical personal protective equipment. Applied Sciences, 12(23):11978, 2022.
57. Li Yang and Abdallah Shami. On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 415:295–316, 2020
Published
2024-08-12
How to Cite
Lahdoudi, Y., Ghazdali, A., Khalfi, H., & Lamghari, N. (2024). Enhancing Mammography Models: The Impact of Radiologist Recommendations on Algorithmic Precision. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-2014
Issue
Section
Research Articles
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
