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Enhanced Ultrasound Breast Cancer Classification Based on Sparse Data and Two Customized Deep Learning Approaches

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Breast cancer is one of the leading causes of death worldwide. Ultrasound images can be seen as an extremely convenient way to diagnose breast tumors, as it can be used alone or in conjunction with a mammogram to determine the nature of a breast lesion. It is a non-ionizing technique, harmless, and very accessible. In the era of artificial intelligence, Diagnosis Aided Systems (CAD) are mostly used to help smart radiologists' interpretation. For this purpose, this manuscript implements two common Deep Learning (DL) approaches to Breast Ultrasound (BUS) images and evaluates their performances in comparison to state-of-the-art results. Two publicly ultrasound breast cancer datasets have been used to compile the final classification model. Dataset consists of a total of 897 images, 537, and 360 images labelled as benign and malignant respectively. Promising results have been obtained from both DL techniques. The custom CNN classifier model has scored 92.53% accuracy and 90.83% sensitivity with a false-positive rate of 6.33%. For Transfer Learning (TL) approach, well-known pre-trained models have been used as feature extractors in addition to a basic classifier built on top. InceptionResNetV2 has been the best-scoring model for this approach, with 91.19% accuracy, 86.38% sensitivity, and a very low false-positive rate of 4.28%. VGG16 and InceptionV3 models also seem to outperform a study in the literature.
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AI; Breast Cancer; BUSI; CNN; Deep Learning; Mendeley; Transfer Learning

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R. L. Siegel, K. D. Miller, and A. Jemal, Cancer statistics, 2015, CA-Cancer J. Clin., vol. 65, no. 1, pp. 5-29, 2015.

C. E. DeSantis, J. Ma, A. Goding Sauer, L. A. Newman, and A. Jemal, Breast cancer statistics, 2017, racial disparity in mortality by state, CA: A Cancer Journal for Clinicians, vol. 67, no. 6, pp. 439-448, 2017.

Who. int. 2021. Breast cancer. [online-Accessed 27 July 2021].
Available at:

Hooley, R.J., Scott, L.M. and Philpotts, L.E., 2013. Breast ultrasonography: state of the art. Radiology, 268(3), pp.642-659.

M. Samulski, R. Hupse, C. Boetes, R.D. Mus, G.J. den Heeten, N. Karssemeijer, Using computer-aided detection in mammography as decision support, Eur Radiol 20 (2010) 2323-2330.

H.-P. Chan, B. Sahiner, M.A. Helvie , N. Petrick, M.A. Roubidoux, T.E. Wilson , et al., Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: a ROC study, Radiology 212 (1999) 817-827.

Giger, M.L. Machine Learning in Medical Imaging. J. Am. Coll. Radiol. 2018, 15, 512-520.

Shan, J.; Alam, S.K.; Garra, B.; Zhang, Y.; Ahmed, T. Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods. Ultrasound Med. Biol. 2016, 42, 980-988.

Zhang, Q.; Xiao, Y.; Dai, W.; Suo, J.; Wang, C.; Shi, J.; Zheng, H. Deep learning based classification of breast tumors with shear-wave elastography. Ultrasonics 2016, 72, 150-157.

Debelee, T.G.; Schwenker, F.; Ibenthal, A.; Yohannes, D. Survey of deep learning in breast cancer image analysis. Evol. Syst. 2020, 11, 143-163.

Jiménez-Gaona, Y., Rodríguez-Álvarez, M.J. and Lakshminarayanan, V., 2020. Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review. Applied Sciences, 10(22), p.8298.

Alzubaidi, L., Zhang, J., Humaidi, A.J. et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8, 53 (2021).

AlZoubi, O., AlAbabneh, N., Hmeidi, I., Bani Yassein, M., A Deep Learning System for the Diagnosis of Heart Problems from ECG Media Files, (2021) International Journal on Communications Antenna and Propagation (IRECAP), 11 (5), pp. 363-371.

Pinzón-Arenas, J., Jiménez-Moreno, R., Pachón-Suescún, C., Handwritten Word Searching by Means of Speech Commands Using Deep Learning Techniques, (2019) International Review on Modelling and Simulations (IREMOS), 12 (4), pp. 253-263.

Jimenez-Moreno, R., Martinez, D., A Novel Parallel Convolutional Network Architecture for Depth-Dependent Object Recognition, (2019) International Review of Automatic Control (IREACO), 12 (2), pp. 76-81.

Thost, V. and Chen, J., 2021. Directed acyclic graph neural networks. arXiv preprint arXiv:2101.07965.

Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S.X., Ni, D. and Wang, T., 2019. Deep learning in medical ultrasound analysis: a review. Engineering, 5(2), pp.261-275.

Van Sloun, R.J., Cohen, R. and Eldar, Y.C., 2019. Deep learning in ultrasound imaging. Proceedings of the IEEE, 108(1), pp.11-29.

Laisné, Marthe (2019), Breast Cancer, Mendeley Data, V1.

Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A. Dataset of breast ultrasound images. Data in Brief. 2020 Feb;28:104863.

Lazo, J.F., Moccia, S., Frontoni, E. and De Momi, E., 2020. Comparison of different CNNs for breast tumor classification from ultrasound images. arXiv preprint arXiv:2012.14517. 2021. Colaboratory - Google. [online- Accessed 1 August 2021]
Available at:

Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), 1-48.

Wang, J., & Perez, L. (2017). The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Networks Vis. Recognit, 11, 1-8. arXiv preprint arXiv: 1712.04621

Canbek, G., Sagiroglu, S., Temizel, T.T. and Baykal, N., 2017, October. Binary classification performance measures/metrics: A comprehensive visualized roadmap to gain new insights. In 2017 International Conference on Computer Science and Engineering (UBMK) (pp. 821-826). IEEE.

Z. Hussain, F. Gimenez, D. Yi, and D. Rubin, Differential data augmentation techniques for medical imaging classification tasks, in AMIA Annual Symposium Proceedings, vol. 2017. American Medical Informatics Association, 2017, p. 979. PMID: 29854165; PMCID: PMC5977656.

Raschka, S., 2018. Model evaluation, model selection, and algorithm selection in machine learning. arXiv preprint arXiv:1811.12808.

Kohavi, R., 1995, August. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai (Vol. 14, No. 2, pp. 1137-1145).

Grunau, G. and Linn, S., 2018. Detection and diagnostic overall accuracy measures of medical tests. Rambam Maimonides medical journal, 9(4).

Al-Dhabyani, W., Gomaa, M., Khaled, H. and Aly, F., 2019. Deep learning approaches for data augmentation and classification of breast masses using ultrasound images. Int. J. Adv. Comput. Sci. Appl, 10(5), pp.1-11.


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