Toward Better Analysis of Breast Cancer Diagnosis: Interpretable AI for Breast Cancer Classification

Alifia Revan Prananda; Eka Legya  Frannita

doi:10.25299/itjrd.2023.11563

Authors

Alifia Revan Prananda Department of Information Technology, Faculty of Engineering, Universitas Tidar
Eka Legya Frannita Department of Leather Product Processing Technology, Politeknik ATK Yogyakarta

DOI:

https://doi.org/10.25299/itjrd.2023.11563

Keywords:

Breast cancer, Classification, Deep learning, Interpretable method

Abstract

Recently, some countries have been distressing with the increasing number of breast cancer cases. Those cases were extremely increased in every year. Practicaly, the increasing number of patients was caused by the manual examination. Recently, some researchers have been done in the development of AI method for solving this problem. However, AI itself still has limitation since it worked in the black-box approach which was difficult to be trusted. Thus, to overcome those problems, we proposed a method that was able to classify breast ultrasound images into two classes (benign and malignant) and able to explain how the prediction was made. Our proposed method consisted of four processes i.e., pre-processing step, development of CNN model, interpretable step and evaluation. In this research work, our proposed method performed into 780 breast ultrasound images divided into three classes (133 normal, 210 malignant, and 437 benign). In the training process, our proposed method obtained training accuracy of 0.9795, training loss of 0.0675. The validation process obtained validation accuracy of 0.8000 and validation loss of 0.5096. While, in the testing process, our proposed method achieved accuracy of 0.7923. In the interpretable process using LIME, the LIME result is covered by doctor visualization. It was indicated that LIME was suitable enough in visualizing the important features of breast cancer severity. Regarding to the results, our proposed method has a potensial to be implemented as an early detection method for classifying malignancy of breast cancer in order to help the doctor in the screening process

Downloads

Download data is not yet available.

References

N. Goyal and M. Chandra Trivedi, “Breast cancer classification and identification using machine learning approaches,” Mater. Today Proc., 2020, doi: https://doi.org/10.1016/j.matpr.2020.10.666.

I. Hirra et al., “Breast Cancer Classification From Histopathological Images Using Patch-Based Deep Learning Modeling,” IEEE Access, vol. 9, pp. 24273–24287, 2021, doi: 10.1109/ACCESS.2021.3056516.

P. Kaur, G. Singh, and P. Kaur, “Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification,” Informatics Med. Unlocked, vol. 16, p. 100239, 2019, doi: https://doi.org/10.1016/j.imu.2019.100239.

C. Dromain, B. Boyer, R. Ferré, S. Canale, S. Delaloge, and C. Balleyguier, “Computed-aided diagnosis (CAD) in the detection of breast cancer,” Eur. J. Radiol., vol. 82, no. 3, pp. 417–423, 2013, doi: https://doi.org/10.1016/j.ejrad.2012.03.005.

T. Ha, Y. Jung, J. Y. Kim, S. Y. Park, D. K. Kang, and T. H. Kim, “Comparison of the diagnostic performance of abbreviated MRI and full diagnostic MRI using a computer-aided diagnosis (CAD) system in patients with a personal history of breast cancer: the effect of CAD-generated kinetic features on reader performance,” Clin. Radiol., vol. 74, no. 10, pp. 817.e15-817.e21, 2019, doi: https://doi.org/10.1016/j.crad.2019.06.025.

C. Kaushal, S. Bhat, D. Koundal, and A. Singla, “Recent Trends in Computer Assisted Diagnosis (CAD) System for Breast Cancer Diagnosis Using Histopathological Images,” IRBM, vol. 40, no. 4, pp. 211–227, 2019, doi: https://doi.org/10.1016/j.irbm.2019.06.001.

B. Zeimarani, M. G. F. Costa, N. Z. Nurani, S. R. Bianco, W. C. D. A. Pereira, and C. F. F. C. Filho, “Breast Lesion Classification in Ultrasound Images Using Deep Convolutional Neural Network,” IEEE Access, vol. 8, pp. 133349–133359, 2020, doi: 10.1109/ACCESS.2020.3010863.

J. Zheng, D. Lin, Z. Gao, S. Wang, M. He, and J. Fan, “Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis,” IEEE Access, vol. 8, pp. 96946–96954, 2020, doi: 10.1109/ACCESS.2020.2993536.

G. Latif, M. O. Butt, F. Y. Al Anezi, and J. Alghazo, “Ultrasound Image Despeckling and detection of Breast Cancer using Deep CNN,” in 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), 2020, pp. 1–5, doi: 10.1109/RIVF48685.2020.9140767.

B. Gong et al., “BI-Modal Ultrasound Breast Cancer Diagnosis Via Multi-View Deep Neural Network SVM,” in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020, pp. 1106–1110, doi: 10.1109/ISBI45749.2020.9098438.

A. Hijab, M. A. Rushdi, M. M. Gomaa, and A. Eldeib, “Breast Cancer Classification in Ultrasound Images using Transfer Learning,” in 2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME), 2019, pp. 1–4, doi: 10.1109/ICABME47164.2019.8940291.

M. Wei, Y. Du, X. Wu, and J. Zhu, “Automatic Classification of Benign and Malignant Breast Tumors in Ultrasound Image with Texture and Morphological Features,” in 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID), 2019, pp. 126–130, doi: 10.1109/ICASID.2019.8925194.

F. A. González-Luna, J. Hernández-López, and W. Gomez-Flores, “A Performance Evaluation of Machine Learning Techniques for Breast Ultrasound Classification,” in 2019 16th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), 2019, pp. 1–5, doi: 10.1109/ICEEE.2019.8884547.

W. Samek, G. Montavon, S. Lapuschkin, C. J. Anders, and K.-R. Müller, “Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications,” Proc. IEEE, vol. 109, no. 3, pp. 247–278, 2021, doi: 10.1109/JPROC.2021.3060483.

Y.-H. Wu et al., “JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation,” IEEE Trans. Image Process., vol. 30, pp. 3113–3126, 2021, doi: 10.1109/TIP.2021.3058783.

M. T. Ribeiro, S. Singh, and C. Guestrin, “‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1135–1144, doi: 10.1145/2939672.2939778.

W. Al-Dhabyani, M. Gomaa, H. Khaled, and A. Fahmy, “Dataset of breast ultrasound images,” Data Br., vol. 28, p. 104863, 2020, doi: https://doi.org/10.1016/j.dib.2019.104863.

H. A. Nugroho, Zulfanahri, E. Frannita, I. Ardiyanto, and L. Choridah, “Computer Aided Diagnosis for Thyroid Cancer System based on Internal and External Characteristics,” J. King Saud Univ. - Comput. Inf. Sci., Jan. 2019, doi: 10.1016/j.jksuci.2019.01.007.

E. R. Davies, “Chapter 3 - Image filtering and morphology,” E. R. B. T.-C. V. (Fifth E. Davies, Ed. Academic Press, 2018, pp. 39–92.