Deep Learning Approaches for Classification of Breast Cancer in Ultrasound (US) Images

Year 2022, Volume: 12 Issue: 4, 1917 - 1927, 01.12.2022

İshak Pacal

https://doi.org/10.21597/jist.1183679

Cited By: 18

Abstract

Breast cancer is one of the deadliest cancer types affecting women worldwide. As with all types of cancer, early detection of breast cancer is of vital importance. Early diagnosis plays an important role in reducing deaths and fighting cancer. Ultrasound (US) imaging is a painless and common technique used in the early detection of breast cancer. In this article, deep learning-based approaches for the classification of breast US images have been extensively reviewed. Classification performance of breast US images of architectures such as AlexNet, VGG, ResNet, GoogleNet and EfficientNet, which are among the most basic CNN architectures, has been compared. Then, transformer models, which are one of the most popular deep learning architectures these days and show similar performance to the performance of CNN' architectures in medical images, are examined. BUSI, the only publicly available dataset, was used in experimental studies. Experimental studies have shown that the transformer and CNN models successfully classify US images of the breast. It has been observed that vision transformer model outperforms other models with 88.6% accuracy, 90.1% precison, 87.4% recall and 88.7% F1-score. This study shows that deep learning architectures are successful in classification of US images and can be used in the clinic experiments in the near future.

Keywords

Deep learning, breast cancer, classification, breast cancer classification, transformer, CNN, VGG, ResNet

References

• AAdem K, Kiliçarslan S. 2021. COVID-19 Diagnosis Prediction in Emergency Care Patients using the Convolutional Neural Network. Afyon Kocatepe University Journal of Sciences and Engineering, 21:300–309.
• Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A. 2020. Dataset of breast ultrasound images. Data in Brief, 28:104863.
• Ayana G, Park J, Jeong JW, Choe SW. 2022. A Novel Multistage Transfer Learning for Ultrasound Breast Cancer Image Classification. Diagnostics, 12(1):1–14.
• Bayat S, Işık G, 2022. Recognition of Aras Bird Species From Their Voices With Deep Learning Methods. Journal of the Institute of Science and Technology, 12(3): 1250 - 1263.
• Chandra R, Divyanshu J, Vaibhav S, Malay T, Dutta K. 2022. An efficient deep neural network based abnormality detection and multi ‑ class breast tumor classification. Multimedia Tools and Applications, 13691–13711.
• Eroğlu Y, Yildirim M, Çinar A. 2021. Convolutional Neural Networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR. Computers in Biology and Medicine, 133(April).
• He K, Zhang X, Ren S, Sun J. 2016. Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem:770–778.
• Işık G, Artuner H. 2020. Turkish dialect recognition in terms of prosodic by long short-term memory neural networks. Journal of the Faculty of Engineering and Architecture of Gazi University, 35(1):213–224.
• Jabeen K, Khan MA, Alhaisoni M, Tariq U, Zhang Y. 2022. Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion.
• Kilicarslan S, Celik M, Sahin afak. 2021. Hybrid models based on genetic algorithm and deep learning algorithms for nutritional Anemia disease classification. Biomedical Signal Processing and Control, 63:1746–8094.
• Kiliçarslan S, Celik M. 2021. RSigELU: A nonlinear activation function for deep neural networks. Expert Systems With Applications, 174:114805.
• Krizhevsky A, Sutskever I, Hinton GE. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In: Pereira F, Burges CJ, Bottou L, Weinberger KQ, Hrsg. Advances in Neural Information Processing Systems. Curran Associates, Inc.;
• Lecun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature, 521(7553):436–444.
• Ozkok FO, Celik M. 2021. Convolutional neural network analysis of recurrence plots for high resolution melting classification. Computer Methods and Programs in Biomedicine, 207:106139.
• Ozkok FO, Celik M. 2022. A hybrid CNN-LSTM model for high resolution melting curve classification. Biomedical Signal Processing and Control, 71:103168.
• Pacal I, Karaboga D. 2021. A robust real-time deep learning based automatic polyp detection system. Computers in Biology and Medicine, 134:104519.
• Pacal I, Karaboga D, Basturk A, Akay B, Nalbantoglu U. 2020. A comprehensive review of deep learning in colon cancer. Computers in Biology and Medicine, 126:104003.
• Pacal I, Karaman A, Karaboga D, Akay B, Basturk A, Nalbantoglu U, Coskun S. 2022. An efficient real-time colonic polyp detection with YOLO algorithms trained by using negative samples and large datasets. Computers in Biology and Medicine, 141(September 2021):105031.
• Pourasad Y, Zarouri E, Parizi MS, Mohammed AS. 2021. Presentation of novel architecture for diagnosis and identifying breast cancer location based on ultrasound images using machine learning. Diagnostics, 11(10).
• Ragab M, Albukhari A, Alyami J, Mansour RF. 2022. Ensemble Deep-Learning-Enabled Clinical Decision Support Ultrasound Images. Biology, 11:439.
• Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, et al. 2015. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3):211–252.
• Seely JM, Alhassan T. 2018. Screening for breast cancer in 2018-what should we be doing today? Seely and Alhassan Current Oncology, 25(1).
• Siegel RL, Miller KD, Fuchs HE, Jemal A. 2022. Cancer statistics, 2022. CA: A Cancer Journal for Clinicians, 72(1):7–33.
• Simonyan K, Zisserman A. 2015. Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 1–14.
• Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. 2015. Going deeper with convolutions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June:1–9.
• Tan M, Pang R, Le Q V. 2020. EfficientDet: Scalable and efficient object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 10778–10787.
• Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. 2017. Attention is all you need. Advances in Neural Information Processing Systems, 2017-Decem(Nips):5999–6009.
• Wang J, Zhu H, Wang SH, Zhang YD. 2021. A Review of Deep Learning on Medical Image Analysis. Mobile Networks and Applications, 26(1):351–380.
• Zhang G, Zhao K, Hong Y, Qiu X, Zhang K, Wei B. 2021. SHA-MTL : soft and hard attention multi-task learning for automated breast cancer ultrasound image segmentation and classification. International Journal of Computer Assisted Radiology and Surgery, 16(10):1719–1725.