Breast Cancer Detection and Classification by Features Non-Linear Mapping with Random Forest Classifier
Keywords:
Mammogram classification, Convolutional neural network, Machine learning, Deep learning, MammogramsAbstract
The current imaging technique of choice for breast cancer screening is mammography. Mammography’s primary encouraging results are masses and calcification. If breast cancer cases are solely relied upon for diagnosis, a sizable portion will be overlooked or incorrectly identified due to the varying appearance of lumps and calcification. Mammography has demonstrated encouraging results with the application of deep learning technology in the quantitative assessment of parenchymal density, categorization, detection, diagnosis, and prognosis of breast cancer risk, allowing more accurate patient management. The idea of deep learning has also improved the workflow efficiency of interpretation by lowering interpretation time and the workload. To definitively demonstrate the efficacy of deep learning, more thorough research is needed. The classification of mammography using a deep learning process is covered in this article. And how it can be used for mammography interpretation, as well as the difficulties it is now facing in actual use. In proposed approach use Long Short-Term Memory (LSTM) based sequence learning and Convolution Neural Network (CNN) based Non-linear feature mapping it improve Mammographic Image Analysis Society (MIAS) dataset accuracy 5%, Precision 6% and recall 4.6%. INBREAST and Digital Database for Screening Mammography (DDSM) dataset using different performance metrics by these experiments validate our approach. In comparison with existing approaches proposed approach improves accuracy by 2-3%, precision by 2% and recall 3-4% in DDSM dataset. It improves accuracy by 3-4%, precision by 2-3%, and recall by 4% in the INBREAST dataset.
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References
Chan HP, Samala RK, Hadjiiski LM, Zhou C. Deep Learning in Medical Image Analysis. Advances in Ex- perimental Medicine and Biology. 2020.
Becker AS, Marcon M, Ghafoor S, Wurnig MC, Frauen- felder T, Boss A. Deep learning in mammography: di- agnostic accuracy of a multipurpose image analysis soft- ware in the detection of breast cancer. Investigative radi- ology. 2017;52(7):434–474.
Zeng Q, Jiang H, Ma L. Learning multi-level features for breast mass detection. ACM International Conference Proceeding Series. 2018
Abdelhafiz D, Yang C, Ammar R, Nabavi S. Deep convolutional neural networks for mammography: ad- vances, challenges and applications. BMC bioinformat- ics. 2019;20(11):1–20.
Mendel K, Li H, Sheth D, Giger M. Transfer learning from convolutional neural networks for computer-aided diagnosis: a comparison of digital breast tomosynthesis and full-field digital mammography. Academic radiol- ogy. 2019;26(6):735–743.
Arevalo J, Gonzalez FA, Ramos-Pollan R, Oliveira JL, Lopez MAG. IEEE; 2018.
Ertosun MG, Rubin DL. Probabilistic visual search for masses within mammography images using deep learn- ing. 2015 IEEE International Conference on Bioinfor- matics and Biomedicine (BIBM). 2015; p. 1310–1315.
Touahri R, Azizi N, Hammami NE, Aldwairi M, Be- naida F. Automated breast tumor diagnosis using local binary patterns (LBP) based on deep learning classifica- tion. 2019 International Conference on Computer and Information Sciences (ICCIS). 2019;p. 1–5.
Suzuki S, Zhang X, Homma N, Ichiji K, Sugita N, Kawa- sumi Y, et al. IEEE; 2016.
Abubaker A, Ghadi YY, Santarisi N. Intelligent computer-aided diagnosis system to enhance mass lesions in digitized mammogram images. International Journal of Electrical and Computer Engineering. 2022;12(3).
Sugiharti E, Arifudin R, Wiyanti DT, Susilo AB. Integration of convolutional neural network and extreme gradient boosting for breast cancer detection. Bulletin of Electrical Engineering and Informatics. 2022;11(2):803– 813.
Abdulla SH, Sagheer AM, Veisi H. Breast cancer segmentation using K-means clustering and optimized region-growing technique. Bulletin of Electrical Engineering and Informatics. 2022;11(1):158–167.
Tabra YM, Tawfeeq FN. Reduced hardware requirements of deep neural network for breast cancer diagnosis. IAES International Journal of Artificial Intelligence. 2022;11(4).
Lim TS, Tay KG, Huong A, Lim XY. Breast cancer diagnosis system using hybrid support vector machine- artificial neural network. International Journal of Electri- cal and Computer Engineering. 2021;11(4):3059–3069.
Ridok A, Widodo N, Mahmudy WF, Rifa’i M. A hybrid feature selection on AIRS method for identifying breast cancer diseases. International Journal of Electrical and Computer Engineering. 2021;11(1).
Kamil MY. Computer-aided diagnosis system for breast cancer based on the Gabor filter technique. International Journal of Electrical and Computer Engineering. 2020;10(5).
Al-Hadidi MR, Alsaaidah B, Al-Gawagzeh MY. Glioblastomas brain tumour segmentation based on convolutional neural networks. International Journal of Electrical and Computer Engineering. 2020;10(5):4738– 4744.
Bagchi S, Tay KG, Huong A, Debnath SK. Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - A review. International Journal of Electrical and Computer Engineering. 2020;10(3).
Croock MS, Khuder SD, Korial AE, Mahmood SS. Early detection of breast cancer using mammography images and software engineering process. Telkomnika (Telecommunication Computing Electronics and Control). 2020;18(4).
Wu H, Gu X. Towards dropout training for convolutional neural networks.” Neural Networks. 2015; 71:1–10.
Singh, J. ., Mani, A. ., Singh, H. ., & Rana, D. S. . (2023). Solution of the Multi-objective Economic and Emission Load Dispatch Problem Using Adaptive Real Quantum Inspired Evolutionary Algorithm. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1s), 01–12. https://doi.org/10.17762/ijritcc.v11i1s.5989
Ana Oliveira, Yosef Ben-David, Susan Smit , Elena Popova, Milica Milić. Machine Learning for Decision Optimization in Complex Systems. Kuwait Journal of Machine Learning, 2(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/201
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