Optimizing CNN Algorithm for Breast Cancer Disease Prediction using ResNet50 with Fine-Tuning Method
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Abstract
The purpose of this research is to optimize and improve performance in predicting breast cancer using CNN Algorithm with ResNet50 architecture and Fine-Tuning Method. The method in this research starts from selecting a dataset based on previous research. The dataset is prepared by performing Pre Processing as data cleaning for file names and strings that contain unnecessary characters and interfere with the analysis. The CNN model is supported by ResNet50 architecture which is initiated by several layer models namely flatten layer, BacthNormalization layer, Dense layer and dropout layer. To improve the accuracy value, Fine Tuning model and early stopping are applied to prevent overfitting. Finally, the prediction is tested by evaluating the model that has been done. The test results show an increase in accuracy compared to previous research. The performance achieved by the model after Fine Tuning is 96.58% accuracy. The novelty of this article is the use of the CNN ResNet50 algorithm and Fine Tuning the model which results in improved accuracy performance in predicting breast cancer disease.
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References
Z. Wang et al., “Breast Cancer Detection Using Extreme Learning Machine Based on Feature Fusion With CNN Deep Features,” vol. 7, 2019.
R. K. Yadav, P. Singh, dan P. Kashtriya, “ScienceDirect Diagnosis of Breast Cancer using Machine Learning Techniques -A Survey,” Procedia Comput. Sci., vol. 218, hal. 1434–1443, 2023, https://doi.org/10.1016/j.procs.2023.01.122.
M. To, “Application of breast cancer diagnosis based on a combination of convolutional neural networks , ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders,” vol. 135, no. October 2019, hal. 0–3, 2020, https://doi.org/10.1016/j.mehy.2019.109503.
K. Loizidou, R. Elia, dan C. Pitris, “Computer-aided breast cancer detection and classification in mammography : A comprehensive review,” Comput. Biol. Med., vol. 153, no. December 2022, hal. 106554, 2023, https://doi.org/10.1016/j.compbiomed.2023.106554.
M. Akram, M. Iqbal, M. Daniyal, dan A. U. Khan, “Awareness and current knowledge of breast cancer,” Biol. Res., hal. 1–23, 2017, https://doi.org/10.1186/s40659-017-0140-9.
P. Gomathi, C. Muniraj, dan P. S. Periasamy, “Biomedical Signal Processing and Control Digital infrared thermal imaging system based breast cancer diagnosis using 4D U-Net segmentation,” Biomed. Signal Process. Control, vol. 85, no. March, hal. 104792, 2023, https://doi.org/10.1016/j.bspc.2023.104792.
Jumanto, M. F. Mardiansyah, R. Pratama, M. F. Al Hakim, dan B. Rawat, “Optimization of breast cancer classification using feature selection on neural network,” hal. 105–110, 2022, https://doi.org/10.52465/joscex.v3i2.78.
A. Brooks et al., “Breast Cancer Early Detection : A Phased Approach to Implementation,” hal. 2379–2393, 2020, https://doi.org/10.1002/cncr.32887.
Q. Huang, Z. Miao, S. Zhou, C. Chang, dan X. Li, “Dense Prediction and Local Fusion of Superpixels : A Framework for Breast Anatomy Segmentation in,” vol. 70, 2021.
A. Ibrahim, S. Mohammed, H. A. Ali, dan S. E. Hussein, “Breast Cancer Segmentation From Thermal Images Based on Chaotic Salp Swarm Algorithm,” hal. 122121–122134, 2020, https://doi.org/10.1109/ACCESS.2020.3007336.
X. Feng, Q. Huang, dan X. Li, “Neurocomputing Ultrasound image de-speckling by a hybrid deep network with transferred filtering and structural prior,” Neurocomputing, vol. 414, hal. 346–355, 2020, https://doi.org/10.1016/j.neucom.2020.09.002.
N. I. R. Yassin, S. Omran, E. M. F. El, dan H. Allam, “Computer Methods and Programs in Biomedicine Machine learning techniques for breast cancer computer aided diagnosis using different image modalities : A systematic review,” Comput. Methods Programs Biomed., vol. 156, hal. 25–45, 2018, https://doi.org/10.1016/j.cmpb.2017.12.012.
M. Minnoor dan V. Baths, “ScienceDirect ScienceDirect Diagnosis of Breast Cancer Using Random Forests Diagnosis of Breast Cancer Using Random Forests,” Procedia Comput. Sci., vol. 218, no. 2022, hal. 429–437, 2023, https://doi.org/10.1016/j.procs.2023.01.025.
V. J. S. Vimal, M. K. Mi, dan Y. Lee, “AI ‑ based smart prediction of clinical disease using random forest classifier and Naive Bayes,” J. Supercomput., vol. 77, no. 5, hal. 5198–5219, 2021, https://doi.org/10.1007/s11227-020-03481-x.
H. D. Cheng, J. Shan, W. Ju, Y. Guo, dan L. Zhang, “Automated breast cancer detection and classification using ultrasound images : A survey,” Pattern Recognit., vol. 43, no. 1, hal. 299–317, 2010, https://doi.org/10.1016/j.patcog.2009.05.012.
L. Tsochatzidis, P. Koutla, L. Costaridou, dan I. Pratikakis, “Computer Methods and Programs in Biomedicine Integrating segmentation information into CNN for breast cancer diagnosis of mammographic masses,” vol. 200, 2021, https://doi.org/10.1016/j.cmpb.2020.105913.
M. Desai dan M. Shah, “Clinical eHealth An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network ( MLP ) and Convolutional neural network ( CNN ),” Clin. eHealth, vol. 4, no. 2021, hal. 1–11, 2022, https://doi.org/10.1016/j.ceh.2020.11.002.
T. Iesmantas dan R. Alzbutas, Convolutional Capsule Network for Classification of Breast Cancer Histology Images. Springer International Publishing, 2018.
G. Huang, Z. Liu, L. Van Der Maaten, dan K. Q. Weinberger, “Densely Connected Convolutional Networks,” 2017, https://doi.org/10.1109/CVPR.2017.243.
D. Yu, J. Lin, T. Cao, Y. Chen, M. Li, dan X. Zhang, “SECS : An effective CNN joint construction strategy for breast cancer histopathological image classification,” J. King Saud Univ. - Comput. Inf. Sci., vol. 35, no. 2, hal. 810–820, 2023, https://doi.org/10.1016/j.jksuci.2023.01.017.
S. Lu, S. Wang, dan Y. Zhang, “BCDNet : An Optimized Deep Network for Ultrasound Breast Cancer Detection,” vol. 44, 2023, https://doi.org/10.1016/j.irbm.2023.100774.
A. M. Ismael dan S. Abdulkadir, “Deep learning approaches for COVID-19 detection based on chest X-ray images,” vol. 164, no. March 2020, 2021, https://doi.org/10.1016/j.eswa.2020.114054.
D. Zhou, “Theory of deep convolutional neural networks : Downsampling,” Neural Networks, vol. 124, hal. 319–327, 2020, https://doi.org/10.1016/j.neunet.2020.01.018.
A. Azzawi et al., “DeepCryoPicker : fully automated deep neural network for single protein particle picking in cryo ‑ EM,” BMC Bioinformatics, hal. 1–38, 2020, https://doi.org/10.1186/s12859-020-03809-7.
T. Wang, C. Lu, M. Yang, F. Hong, dan C. Liu, “A hybrid method for heartbeat classi fi cation via convolutional neural networks , multilayer perceptrons and focal loss,” no. Cvd, hal. 1–17, 2020, https://doi.org/10.7717/peerj-cs.324.
K. I. Sundus, B. H. Hammo, M. B. Al-zoubi, dan A. Al-omari, “Informatics in Medicine Unlocked Solving the multicollinearity problem to improve the stability of machine learning algorithms applied to a fully annotated breast cancer dataset,” Informatics Med. Unlocked, vol. 33, no. July, hal. 101088, 2022, https://doi.org/10.1016/j.imu.2022.101088.
D. Evans dan T. Berg-kirkpatrick, “Memorization in NLP Fine-tuning Methods,” 2022.
S. Kim dan K. Kwak, “Incremental modeling with rough and fine tuning method,” vol. 11, hal. 585–591, 2011, https://doi.org/10.1016/j.asoc.2009.12.017.
M. Pham, Y. Ha, dan Y. Kim, “Automatic detection and measurement of ground crack propagation using deep learning networks and an image processing technique,” Measurement, vol. 215, no. January, hal. 112832, 2023, https://doi.org/10.1016/j.measurement.2023.112832.
A. Chen et al., “A Comprehensive Comparative Study of Deep Learning Methods for Noisy Sperm Image Classification : from Convolutional Neural Network to Visual Transformer,” Intell. Med., hal. 0–43, 2023, https://doi.org/10.1016/j.imed.2023.04.001.
C. Huertas, B. Inf, D. F. C. Huertas, A. M. Á. Meza, C. D. A. Medina, dan G. A. C. Duque, “CNN ‑ based framework using spatial dropping for enhanced interpretation of neural activity in motor imagery classification,” Brain Informatics, 2020, https://doi.org/10.1186/s40708-020-00110-4.