A Vision Transformer Network With Wavelet-Based Features for Breast Ultrasound Classification
DOI:
https://doi.org/10.5566/ias.3116Keywords:
Breast cancer, Convolutional neural networks, Deep learning, Ultrasound, Vision-TransformerAbstract
Breast cancer is a prominent contributor to mortality associated with cancer in the female population on a global scale. The timely identification and precise categorization of breast cancer are of utmost importance in enhancing patient prognosis. Nevertheless, the task of precisely categorizing breast cancer based on ultrasound imaging continues to present difficulties, primarily due to the presence of dense breast tissues and their inherent heterogeneity. This study presents a unique approach for breast cancer categorization utilizing the wavelet based vision transformer network. To enhance the neural network’s receptive fields, we have incorporated the discrete wavelet transform (DWT) into the network input. This technique enables the capture of significant features in the frequency domain. The proposed model exhibits the capability to effectively capture intricate characteristics of breast tissue, hence enabling correct classification of breast cancer with a notable degree of precision and efficiency. We utilized two breast tumor ultrasound datasets, including 780 cases from Baheya hospital in Egypt and 267 patients from the UDIAT Diagnostic Centre of Sabadell in Spain. The findings of our study indicate that the proposed transformer network achieves exceptional performance in breast cancer
classification. With an AUC rate of 0.984 and 0.968 on both datasets, our approach surpasses conventional deep learning techniques, establishing itself as the leading method in this domain. This study signifies a noteworthy advancement in the diagnosis and categorization of breast cancer, showcasing the potential of the proposed transformer networks to enhance the efficacy of medical imaging analysis.
References
Abdel-Nasser M, Melendez J, Moreno A, Omer OA, Puig D (2017). Breast tumor classification in ultrasound images using texture analysis and super-resolution methods. Engineering Applications of Artificial Intelligence 59:84–92.
Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A (2020). Dataset of breast ultrasound images. Data in brief 28:104863.
Ali A, Touvron H, Caron M, Bojanowski P, Douze M, Joulin A, Laptev I, Neverova N, Synnaeve G, Verbeek J, et al. (2021). Xcit: Crosscovariance image transformers. Advances in neural information processing systems 34:20014– 27.
Byra M (2021). Breast mass classification with transfer learning based on scaling of deep representations. Biomedical Signal Processing and Control 69:102828. Byra M, Andre M (2019). Breast mass classification in ultrasound based on kendall’s shape manifold. arXiv preprint arXiv190511159 .
Byra M, Galperin M, Ojeda-Fournier H, Olson L, O’Boyle M, Comstock C, Andre M (2019). Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion. Medical physics 46:746–55.
Chowdhury A, Razzaque RR, Muhtadi S, Shafiullah A, Abir EUI, Garra BS, Alam SK (2022). Ultrasound classification of breast masses using a comprehensive nakagami imaging and machine learning framework. Ultrasonics 124:106744.
Das A, Rana S (2021). Exploring residual networks for breast cancer detection from ultrasound images. In: 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE.
Daubechies I (1990). The wavelet transform, timefrequency localization and signal analysis. IEEE transactions on information theory 36:961–1005.
Duffy SW, Tab´ar L, Chen HH, Holmqvist M, Yen MF, Abdsalah S, Epstein B, Frodis E, Ljungberg E, Hedborg-Melander C, et al. (2002). The impact of organized mammography service screening on breast carcinoma mortality in seven swedish counties: A collaborative evaluation. Cancer Interdisciplinary International Journal of the American Cancer Society 95:458–69.
Fan Z, Gong P, Tang S, Lee CU, Zhang X, Song P, Chen S, Li H (2023). Joint localization and classification of breast masses on ultrasound images using an auxiliary attention-based framework. Medical image analysis 90:102960. Ge S, Ye Q, Xie W, Sun D, Zhang H, Zhou X, Yuan K (2023). Ai-assisted method for efficiently generating breast ultrasound screening reports. Current Medical Imaging 19:149–57.
Gheflati B, Rivaz H (2022). Vision transformers for classification of breast ultrasound images. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE.
Hassanien MA, Singh VK, Puig D, Abdel-Nasser M (2022). Predicting breast tumor malignancy using deep convnext radiomics and quality-based score pooling in ultrasound sequences. Diagnostics 12:1053.
Kalafi EY, Jodeiri A, Setarehdan SK, Lin NW, Rahmat K, Taib NA, Ganggayah MD, Dhillon SK (2021). Classification of breast cancer lesions in ultrasound images by using attention layer and loss ensemble in deep convolutional neural networks. Diagnostics 11:1859.
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, S´anchez CI (2017). A survey on deep learning in medical image analysis. Medical image analysis 42:60–88.
Luo Y, Huang Q, Li X (2022). Segmentation information with attention integration for classification of breast tumor in ultrasound image. Pattern Recognition 124:108427.
Mo Y, Han C, Liu Y, Liu M, Shi Z, Lin J, Zhao B, Huang C, Qiu B, Cui Y, et al. (2023). Hovertrans: Anatomy-aware hover-transformer for roifree breast cancer diagnosis in ultrasound images. IEEE Transactions on Medical Imaging .
Moon WK, Lee YW, Ke HH, Lee SH, Huang CS, Chang RF (2020). Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks. Computer methods and programs in biomedicine 190:105361.
Nemat H, Fehri H, Ahmadinejad N, Frangi AF, Gooya A (2018). Classification of breast lesions in ultrasonography using sparse logistic regression and morphology-based texture features. Medical physics 45:4112–24.
Ning Z, Tu C, Xiao Q, Luo J, Zhang Y (2020). Multi-scale gradational-order fusion framework for breast lesions classification using ultrasound images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer.
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition.
Shamshad F, Khan S, Zamir SW, Khan MH, Hayat M, Khan FS, Fu H (2023). Transformers in medical imaging: A survey. Medical Image Analysis :102802.
Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence. Tan M, Le Q (2021). Efficientnetv2: Smaller models and faster training. In: International Conference on Machine Learning. PMLR.
Vigil N, Barry M, Amini A, Akhloufi M, Maldague XP, Ma L, Ren L, Yousefi B (2022). Dual-intended deep learning model for breast cancer diagnosis in ultrasound imaging. Cancers 14:2663. Wang L (2017). Early diagnosis of breast cancer. Sensors 17:1572.
Wei M, Du Y, Wu X, Su Q, Zhu J, Zheng L, Lv G, Zhuang J (2020). A benign and malignant breast tumor classification method via efficiently combining texture and morphological features on ultrasound images. Computational and Mathematical Methods in Medicine 2020.
Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, Xie S (2023). Convnext v2: Co-designing and scaling convnets with masked autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
Xie S, Girshick R, Doll´ar P, Tu Z, He K (2017). Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition.
Yap MH, Pons G, Mart´ı J, Ganau S, Sentis M, Zwiggelaar R, Davison AK, Marti R (2017). Automated breast ultrasound lesions detection using convolutional neural networks. IEEE journal of biomedical and health informatics 22:1218–26.
Zourhri M, Hamida S, Akouz N, Cherradi B, Nhaila H, El Khaili M (2023). Deep learning technique for classification of breast cancer using ultrasound images. In: 2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). IEEE.
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Copyright (c) 2024 Chenyang He, Yan Diao, Xingcong Ma, Shuo Yu, Xin He, Guochao Mao, Xinyu Wei, Yu Zhang, Yang Zhao
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